D. Goldberg

D. Goldberg, D. Thierens, A. G. Pereira, 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

Analysis of Mixing in Genetic Algorithms: A Survey

D. Goldberg, K. Sastry, 2002 .

On the supply of building blocks

Thomas D. LaToza, D. Goldberg, K. Sastry, 2001 .

Don't evaluate, inherit

D. Goldberg, M. Pelikán, K. Sastry, 2001 .

A niched Pareto genetic algorithm for multiobjective optimization

David E. Goldberg, Jeffrey Horn, Nicholas Nafpliotis, 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

Signal, noise, and genetic algorithms

D. Goldberg, M. Rudnick, 1991 .

The Theory of Virtual Alphabets

David E. Goldberg, D. Goldberg, 1990, PPSN.

Messy Genetic Algorithms: Motivation, Analysis, and First Results

Kalyanmoy Deb, David E. Goldberg, Bradley Korb, 1989, Complex Syst..

An Investigation of Niche and Species Formation in Genetic Function Optimization

Kalyanmoy Deb, David E. Goldberg, D. Goldberg, 1989, ICGA.

BOA: the Bayesian optimization algorithm

D. Goldberg, M. Pelikán, E. Cantú-Paz, 1999 .

Tree network design with genetic algorithms an investigation in the locality of the pruefernumber en

D. Goldberg, Franz Rothlauf, 1999 .

Is Engineering Philosophically Weak

David E. Goldberg, D. Goldberg, 2013 .

On becoming a reflective practitioner

David E. Goldberg, D. Goldberg, 2018, GECCO.

Application of evolutionary algorithms and neural network concepts to the design of low-cost, wideband antenna arrays

David E. Goldberg, Scott Santarelli, Tian-Li Yu, 2007, SPIE Defense + Commercial Sensing.

Pruefer Numbers and Genetic Algorithms: A Lesson on How the Low Locality of an Encoding Can Harm the Performance of GAs

Franz Rothlauf, David E. Goldberg, D. Goldberg, 2000, PPSN.

The importance of pairwork in interdisciplinary and educational initiatives

David E. Goldberg, D. Goldberg, 2009, 2009 39th IEEE Frontiers in Education Conference.

Probabilistic Model Building and Competent Genetic Programming

D. Goldberg, K. Sastry, 2003 .

Compressed introns in a linkage learning genetic algorithm

Fernando G. Lobo, D. Goldberg, K. Deb, 1998 .

A Survey of Optimization by Building and Using Probabilistic Models

David E. Goldberg, Martin Pelikan, Fernando G. Lobo, 2002, Comput. Optim. Appl..

Bayesian optimization algorithm, decision graphs, and Occam's razor

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2001 .

A Survey of Optimization by Building and Using Probabilistic Models

Fernando G. Lobo, D. Goldberg, M. Pelikán, 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

Linkage Problem, Distribution Estimation, and Bayesian Networks

David E. Goldberg, Martin Pelikan, Erick Cantú-Paz, 2000, Evolutionary Computation.

Scalability of the Bayesian optimization algorithm

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2002, Int. J. Approx. Reason..

Sizing Populations for Serial and Parallel Genetic Algorithms

David E. Goldberg, D. Goldberg, 1989, ICGA.

Massive Multimodality, Deception, and Genetic Algorithms

Kalyanmoy Deb, David E. Goldberg, Jeffrey Horn, 1992, PPSN.

Implicit Niching in a Learning Classifier System: Nature's Way

David E. Goldberg, Kalyanmoy Deb, Jeffrey Horn, 1994, Evolutionary Computation.

Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis

David E. Goldberg, D. Goldberg, 1989, Complex Syst..

Real-coded Genetic Algorithms, Virtual Alphabets, and Blocking

David E. Goldberg, D. Goldberg, 1991, Complex Syst..

Introducing Start Expression Genes to the Linkage Learning Genetic Algorithm

David E. Goldberg, Ying-Ping Chen, D. Goldberg, 2002, PPSN.

Linkage learning through probabilistic expression

D. Goldberg, G. Harik, 2000 .

Alleles, loci and the traveling salesman problem

David E. Goldberg, D. Goldberg, 1985 .

A Critical Review of Classifier Systems

David E. Goldberg, Stewart W. Wilson, D. Goldberg, 1989, ICGA.

Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule

Martin V. Butz, David E. Goldberg, Jaume Bacardit, 2005, IWLCS.

Ordering Genetic Algorithms and Deception

Kalyanmoy Deb, David E. Goldberg, Hillol Kargupta, 1992, PPSN.

Adaptive Niching via coevolutionary Sharing

D. Goldberg, Liqiang Wang, 1997 .

Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement

David E. Goldberg, Ole J. Mengshoel, D. Goldberg, 1999 .

Convergence Models of Genetic Algorithm Selection Schemes

Dirk Thierens, David E. Goldberg, D. Goldberg, 1994, PPSN.

What Is a Learning Classifier System?

Marco Colombetti, Marco Dorigo, David E. Goldberg, 1999, Learning Classifier Systems.

An investigation of messy genetic algorithms

D. Goldberg, K. Deb, B. Korb, 1990 .

Learning Linkage

David E. Goldberg, Georges R. Harik, D. Goldberg, 1996, FOGA.

Analyzing Deception in Trap Functions

Kalyanmoy Deb, David E. Goldberg, D. Goldberg, 1992, FOGA.

Accounting for Noise in the Sizing of Populations

Kalyanmoy Deb, David E. Goldberg, James H. Clark, 1992, FOGA.

Genetic algorithms and Machine Learning

David E. Goldberg, John H. Holland, J. Holland, 1988, Machine Learning.

The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations

David E. Goldberg, Erick Cantú-Paz, Brad L. Miller, 1999, Evolutionary Computation.

Computer-aided pipeline operation using genetic algorithms and rule learning. PART II: Rule learning control of a pipeline under normal and abnormal conditions

David E. Goldberg, D. Goldberg, 1987, Engineering with Computers.

Finite Markov Chain Analysis of Genetic Algorithms

David E. Goldberg, Philip Segrest, D. Goldberg, 1987, ICGA.

Elitist recombination: an integrated selection recombination GA

Dirk Thierens, David E. Goldberg, D. Goldberg, 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy

David E. Goldberg, Robert E. Smith, D. Goldberg, 1987, ICGA.

ENGINEERING OPTIMIZATION VIA GENETIC ALGORITHM, IN WILL

David E. Goldberg, Manohar P. Samtani, D. Goldberg, 1986 .

Genetic Algorithms and Computer-Assisted Music Composition

David E. Goldberg, Andrew Horner, D. Goldberg, 1991, ICMC.

Mixing in Genetic Algorithms

Dirk Thierens, David E. Goldberg, D. Goldberg, 1993, ICGA.

Reinforcement learning with classifier systems

David E. Goldberg, Robert E. Smith, D. Goldberg, 1990, Proceedings [1990]. AI, Simulation and Planning in High Autonomy Systems.

XCS with Computable Prediction in Multistep Environments

Stewart W. Wilson, D. Goldberg, D. Loiacono, 2005 .

Genetic Algorithm Difficulty and the Modality of Fitness Landscapes

David E. Goldberg, Jeffrey Horn, D. Goldberg, 1994, FOGA.

What Makes a Problem Hard for a Classifier System

David E. Goldberg, Kalyanmoy Deb, Jeffrey Horn, 1992 .

On the Homogenization of Data from Two Laboratories Using Genetic Programming

Jose Garcia Moreno-Torres, Xavier Llorà, David E. Goldberg, 2009, IWLCS.

Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection

David E. Goldberg, Xavier Llorà, Ivan Traus, 2002, IWLCS.

Minimal Achievable Error in the LED problem

D. Goldberg, Xavier Lloraand, 2002 .

Knowledge Extraction and Problem Structure Identification in XCS

Martin V. Butz, Xavier Llorà, David E. Goldberg, 2004, PPSN.

Bounding Learning Time in XCS

D. Goldberg, Martin Volker Butz, P. Lanzi, 2004, GECCO.

Calculating Efficient Team Size: Balancing Deciding and Doing as an Elementary Optimization Problem

David E. Goldberg, Tian-Li Yu, Ali A. Yassine, 2004 .

AllelesLociand the Traveling Salesman Problem

David E. Goldberg, Robert Lingle, D. Goldberg, 1985, ICGA.

Designing efficient and accurate parallel genetic algorithms (parallel algorithms)

David E. Goldberg, Erick Cantú-Paz, D. Goldberg, 1999 .

iBOA: the incremental bayesian optimization algorithm

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2008, GECCO '08.

Substructural Surrogates for Learning Decomposable Classification Problems

David E. Goldberg, Kumara Sastry, Ester Bernadó-Mansilla, 2008, IWLCS.

A matrix approach for finding extrema: problems with modularity, hierarchy, and overlap

D. Goldberg, Tian-Li Yu, 2006 .

Efficient evaluation relaxation under integrated fitness functions

David E. Goldberg, Laura A. Albert, D. Goldberg, 2001 .

Bounding the Population Size to Ensure Niche Support in XCS

D. Goldberg, Martin Volker Butz, P. Lanzi, 2004 .

Efficient Evaluation Genetic Algorithms under Integrated Fitness Functions

David E. Goldberg, Laura A. Albert, D. Goldberg, 2001 .

The First-to-Fourth Flatline: Assessing Undergraduate Students' Creative Capacity

David E. Goldberg, Holli Burgon, J. Bruce Elliott-Litchfield, 2011 .

Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '00), Las Vegas, Nevada, USA, July 8-12, 2000

David E. Goldberg, Lee Spector, Hans-Georg Beyer, 2002, GECCO.

Getting the best of both worlds: Discrete and continuous genetic and evolutionary algorithms in concert

David E. Goldberg, Martin Pelikan, Shigeyoshi Tsutsui, 2003, Inf. Sci..

Inducing Sequentiality Using Grammatical Genetic Codes

David E. Goldberg, Kumara Sastry, Kei Ohnishi, 2004, GECCO.

Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection

Martin V. Butz, David E. Goldberg, Kumara Sastry, 2005, Genetic Programming and Evolvable Machines.

Special session — A war of words: Using sticky language to effect change in engineering education

David E. Goldberg, Russell Korte, Mark H. Somerville, 2010, 2010 IEEE Frontiers in Education Conference (FIE).

Limits of scalability of multiobjective estimation of distribution algorithms

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2005, 2005 IEEE Congress on Evolutionary Computation.

An Analysis of a Reordering Operator with Tournament Selection on a GA-Hard Problem

David E. Goldberg, Ying-Ping Chen, D. Goldberg, 2003, GECCO.

Population Sizing for Genetic Programming Based Upon Decision Making pdf

David E. Goldberg, Kumara Sastry, Una-May O'Reilly, 2005, ArXiv.

How Fitness Structure Affects Subsolution Acquisition in Genetic Programming

D. Goldberg, 1998 .

Genetic Programming 1998: Proceedings of the Third Annual Conference

Hitoshi Iba, David E. Goldberg, Kalyanmoy Deb, 1999, IEEE Trans. Evol. Comput..

Monte Carlo Simulation of Dynamical Systems of Engineering Interest in a Massively Parallel Computing Environment: an Application of Genetic Algorithms

Shirley J. Dyke, David E. Goldberg, Erik A. Johnson, 1996 .

Repairing fractures between data using genetic programming-based feature extraction: A case study in cancer diagnosis

Jose Garcia Moreno-Torres, Xavier Llorà, David E. Goldberg, 2013, Inf. Sci..

Effects of a deterministic hill climber on hBOA

David E. Goldberg, Martin Pelikan, Elizabeth Radetic, 2009, GECCO.

Verification and extension of the theory of global-local hybrids

D. Goldberg, A. Sinha, 2001 .

Creating an intrinsic-motivation-driven course design method

David E. Goldberg, Mark H. Somerville, Geoffrey L. Herman, 2013, 2013 IEEE Frontiers in Education Conference (FIE).

Spin-Flip Symmetry and Synchronization

David E. Goldberg, Bart Naudts, Clarissa Van Hoyweghen, 2002, Evolutionary Computation.

Building block superiority, multimodality and synchronization problems

D. Goldberg, B. Naudts, C. V. Hoyweghen, 2001 .

Genetic Algorithms, Noise, and the Sizing of Populations

Kalyanmoy Deb, David E. Goldberg, James H. Clark, 1992, Complex Syst..

Modeling tournament selection with replacement using apparent added noise

D. Goldberg, K. Sastry, 2001 .

Predicting Speedups of Idealized Bounding Cases of Parallel Genetic Algorithms

D. Goldberg, E. Cant, 1997 .

Bounding the Effect of Noise in Multiobjective Learning Classifier Systems

Xavier Llorà, David E. Goldberg, D. Goldberg, 2003, Evolutionary Computation.

ClusterMI: Building Probabilistic Models Using Hierarchical Clustering and Mutual Information

D. Goldberg, Thyago Duque, 2010 .

Multiobjective Estimation of Distribution Algorithms

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2006, Scalable Optimization via Probabilistic Modeling.

When Huge Is Routine: Scaling Genetic Algorithms and Estimation of Distribution Algorithms via Data-Intensive Computing

Xavier Llorà, David E. Goldberg, Roy H. Campbell, 2010, Parallel and Distributed Computational Intelligence.

The Design of Innovation

David E. Goldberg, D. Goldberg, 2002, Genetic Algorithms and Evolutionary Computation.

Verification of the theory of genetic algorithm continuation

D. Goldberg, Ravi Srivastava, 2001 .

A self adaptive hybrid genetic algorithm

David E. Goldberg, Barbara S. Minsker, Felipe P. Espinoza, 2001 .

XCS with computed prediction for the learning of Boolean functions

Daniele Loiacono, David E. Goldberg, Stewart W. Wilson, 2005, 2005 IEEE Congress on Evolutionary Computation.

Performance Evaluation and Population Reduction for a Self Adaptive Hybrid Genetic Algorithm (SAHGA)

David E. Goldberg, Barbara S. Minsker, Felipe P. Espinoza, 2003, GECCO.

Extracted global structure makes local building block processing effective in XCS

Martin V. Butz, Xavier Llorà, David E. Goldberg, 2005, GECCO '05.

Sporadic model building for efficiency enhancement of hierarchical BOA

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2006, GECCO.

Research on the Bayesian Optimization Algorithm Research on the Bayesian Optimization Algorithm

D. Goldberg, M. Pelikán, 2022 .

Designing a new elitist Nondominated Sorted Genetic Algorithm for a multiobjective long term groundwater monitoring application

David E. Goldberg, Patrick M. Reed, Barbara S. Minsker, 2001 .

A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data

D. Goldberg, B. Minsker, P. Reed, 2001 .

Genetic Algorithms and Walsh Functions: Part I, A Gentle Introduction

David E. Goldberg, D. Goldberg, 1989, Complex Syst..

Influence of selection and replacement strategies on linkage learning in BOA

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2007, 2007 IEEE Congress on Evolutionary Computation.

Population Sizing for Entropy-based Model Building in Genetic Algorithms

D. Goldberg, M. Pelikán, K. Sastry, 2006 .

Decomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms pdf

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2005, ArXiv.

The Anticipatory Classiier System and Genetic Generalization the Anticipatory Classiier System and Genetic Generalization

D. Goldberg, GeneralizationMartin V. Butz, 2007 .

Designing Efficient Genetic and Evolutionary Algorithm Hybrids

David E. Goldberg, D. Goldberg, Ying-Ping Chen, 2005 .

Diting : An Author Disambiguation method based on Network Representation Learning

D. Goldberg, Bing Liu, F. Mueller, 2019 .

Sporadic Model Building for Efficiency Enhancement of hBOA Martin Pelikan

D. Goldberg, M. Pelikán, 2005 .

Frontiers of Evolutionary Computation

D. Goldberg, Anil Menon, 2004, Genetic Algorithms and Evolutionary Computation.

A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms

D. Goldberg, K. Sastry, Tian-Li Yu, 2007 .

How Well Does A Single-Point Crossover Mix Building Blocks with Tight Linkage?

D. Goldberg, K. Sastry, 2004 .

Investigating Generalization in the Anticipatory Classifier System

D. Goldberg, Martin Volker Butz, W. Stolzmann, 2000, PPSN.

New Challenges for an Anticipatory Classiier System: Hard Problems and Possible Solutions New Challenges for an Anticipatory Classiier System: Hard Problems and Possible Solutions

D. Goldberg, Martin Volker Butz, W. Stolzmann, 2007 .

Solving permutation problems with the ordering messy genetic algorithm

D. Goldberg, Dimitri Knjazew, 2003 .

Deceptive and Other Functions of Unitation as Bayesian Networks

D. C. Wilkins, D. Goldberg, O. Mengshoel, 1998 .

Evolutionary Algorithm Using Marginal Histogram Models in Continuous Domain

D. Goldberg, M. Pelikán, S. Tsutsui, 2007 .

Natural niching for evolving cooperative classifiers

D. Goldberg, Jeffrey D. Horn, 1996 .

RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms

Kalyanmoy Deb, David E. Goldberg, Hillol Kargupta, 1993, ICGA.

Tightness Timefor the Linkage Learning

D. Goldberg, Ying-ping Chen, 2003 .

DOUBLE DUTY: GENETIC ALGORITHMS FOR ORGANIZATIONAL DESIGN AND GENETIC ALGORITHMS INSPIRED BY ORGANIZATIONAL THEORY

D. Goldberg, Tian-Li Yu, A. Yassine, 2005 .

Toward a Mechanics of Conceptual Machines

David E. Goldberg, D. Goldberg, 1995 .

The Existential Pleasures of Genetic Algorithms the Existential Pleasures of Genetic Algorithms

David E. Goldberg, Samuel Florman, D. Goldberg, 1994 .

Node Histogram vs . Edge Histogram : A Comparison of PMBGAs in Permutation Domains

David E. Goldberg, Martin Pelikan, Shigeyoshi Tsutsui, 2006 .

Aalborg Universitet Dynamic Uniform Scaling for Multiobjective Genetic Algorithms

D. Goldberg, G. Pedersen, 2022 .

Scaling eCGA model building via data-intensive computing

Xavier Llorà, David E. Goldberg, Roy H. Campbell, 2010, IEEE Congress on Evolutionary Computation.

Solving Sequence Problems by Building and Sampling Edge Histograms

David E. Goldberg, Martin Pelikan, Shigeyoshi Tsutsui, 2002 .

Local Search Issues for the Application of a Self-Adaptive Hybrid Genetic Algorithm in Groundwater Remediation Design

D. Goldberg, B. Minsker, F. P. Espinoza, 2003 .

Introdu ing Start Expression Genes to theLinkage Learning

D. Goldberg, Geneti AlgorithmYing-ping Chen, 2002 .

Analysis and Improvement of Fitness Exploitation in XCS : Bounding Models , Tournament Sele tion , and Bilateral A ura yMartin

D. Goldberg, Kurian, V. Butz, 2003 .

Message Feature Map toward Effective Facilitation on On-line Discussions

D. Goldberg, Xavier Llorà, N. Yasui, 2007 .

Human-Centered Analysis and Visualization Tools for the Blogosphere

D. Goldberg, Xavier Llorà, M. Welge, 2006 .

Genetic algorithms and Machine Learning

D. Goldberg, 2004, Machine Learning.

Toward routine billion-variable optimization using genetic algorithms

Xavier Llorà, David E. Goldberg, Kumara Sastry, 2007, Complex..

Real-Coded Extended Compact Genetic Algorithm

David E. Goldberg, Kumara Sastry, Pier Luca Lanzi, 2007 .

Gradient Descent Methods in Learning Classifier Systems

Martin V. Butz, David E. Goldberg, Pier Luca Lanzi, 2004 .

Investigating Generalization in the Anticipatory Classiier System Investigating Generalization in the Anticipatory Classiier System

D. Goldberg, Martin Volker Butz, W. Stolzmann, 2007 .

Introducing a Genetic Generalization Pressure to the Anticipatory Classiier System Part 1: Theoretical Approach Introducing a Genetic Generalization Pressure to the Anticipatory Classiier System Part 1: Theoretical Approach

D. Goldberg, Martin Volker Butz, W. Stolzmann, 2007 .

Eeciency Enhancement of Probabilistic Model Building Genetic Algorithms Eeciency Enhancement of Probabilistic Model Building Genetic Algorithms

D. Goldberg, M. Pelikán, K. Sastry, 2022 .

Eecient Parallel Genetic Algorithms: Theory and Practice

D. Goldberg, E. Cant, 2000 .

MGA in C: A Messy Genetic Algorithm in C

David E. Goldberg, Kalyanmoy Deb, D. Goldberg, 1991 .

Blackbox Optimization , And Sample Complexity

D. Goldberg, H. Kargupta, 2008 .

Bayesian Optimization Algorithm: From Single Level to Hierarchy

David E. Goldberg, Martin Pelikan, D. Goldberg, 2002 .

2 Learning Classifier Systems for Hyperspectral Images Processing

D. Goldberg, Martin Volker Butz, J. Korczak, 2004 .

Multiple-objective Bayesian Optimization Algorithm

David E. Goldberg, Martin Pelikan, Nazan Khan, 2002, GECCO.

Effective and Reliable Online Classification Combining XCS with EDA Mechanisms

Martin V. Butz, Xavier Llorà, David E. Goldberg, 2006, Scalable Optimization via Probabilistic Modeling.

Two analysis tools to describe the operation of classifier systems

David E. Goldberg, Manuel Valenzuela-Rendon, D. Goldberg, 1989 .

The Entrepreneurial Engineer

David E. Goldberg, D. Goldberg, 2006 .

A Genetic Algorithm for Parallel Simulated Annealing

David E. Goldberg, Samir W. Mahfoud, D. Goldberg, 1992, PPSN.

Military Antenna Design Using a Simple Genetic Algorithm and hBOA

David E. Goldberg, Scott Santarelli, Tian-Li Yu, 2006, Scalable Optimization via Probabilistic Modeling.

Foreword

David E. Goldberg, D. Goldberg, 2003, EURASIP J. Adv. Signal Process..

Change in Engineering Education: One Myth, Two Scenarios, and Three Foci

David E. Goldberg, D. Goldberg, 1996 .

Simple adaptive control of a robot arm

David E. Goldberg, Yoram Koren, A. G. Ulsoy, 1982 .

Genetic and Evolutionary Algorithms in the Real World

David E. Goldberg, D. Goldberg, 1999 .

Computational Complexity of the XCS Classifier System

Martin V. Butz, David E. Goldberg, Pier Luca Lanzi, 2005 .

Discovering Building Blocks for Human Based Genetic Algorithms

David E. Goldberg, Kumara Sastry, Xavier Llora, 2007 .

First Cognitive Capabilities in the Anticipatory Classifier System

Martin V. Butz, David E. Goldberg, Joachim Hoffmann, 2000 .

Philosophy and engineering : an emerging agenda

David E. Goldberg, Ibo van de Poel, D. Goldberg, 2010 .

Real-coded ECGA for solving decomposable real-valued optimization problems

David E. Goldberg, Kumara Sastry, Minqiang Li, 2007, 2007 IEEE Congress on Evolutionary Computation.

Compact genetic algorithm: A litmus test for genetic algorithm applicability

David E. Goldberg, Eric Michielssen, Daniel S. Weile, 2000 .

Genetic Algorithms and Rules Learning in Dynamic System Control

David E. Goldberg, D. Goldberg, 1985, ICGA.

Getting Organized and Finding Time

David E. Goldberg, D. Goldberg, 2005 .

Binary rule encoding schemes: a study using the compact classifier system

Xavier Llorà, David E. Goldberg, Kumara Sastry, 2005, GECCO '05.

Opening Plenary--Keynote Address (Alumni Center Assembly Hall) A Practical, Comprehensive Decomposition of the Forces, Foci, and Processes for Engineering Education Transformation

David E. Goldberg, D. Goldberg, 2014 .

Entrepreneurial Engineer: Ready for the 21st Century

David E. Goldberg, D. Goldberg, 2005 .

Assessing Technology Opportunities

David E. Goldberg, D. Goldberg, 2005 .

Genetic Algorithms: A Bibliography

David E. Goldberg, Kelsey Milman, D. Goldberg, 1997 .

Diploidy and Dominance in Artificial Genetic Search

David E. Goldberg, Robert E. Smith, D. Goldberg, 1992, Complex Syst..

Present, Don't Speak

David E. Goldberg, D. Goldberg, 2005 .

Toward a Better Understanding of Mixing in Genetic Algorithms

Dirk Thierens, David E. Goldberg, Kalyanmoy Deb, 1993 .

The Making of A Whole New Engineer: Four Unexpected Lessons for Engineering Educators and Education Researchers

David E. Goldberg, Mark Somerville, D. Goldberg, 2015 .

Takeover Time in a Noisy Environment

David E. Goldberg, Yuji Sakamoto, D. Goldberg, 1997, ICGA.

Money, Work, and You

David E. Goldberg, D. Goldberg, 2005 .

Zen and the Art of Genetic Algorithms

David E. Goldberg, D. Goldberg, 1989, ICGA.

Genetic Algorithm-Based Searching Method for Piping Path Routing in Coal-Fired Boiler Buildings

David E. Goldberg, Naohiro Kusumi, Noriyuki Ichinose, 2011 .

Students As The Key To Unleashing Student Engagement: The Theory, Design, & Launch Of A Scalable, Student Run Learning Community At Xx

David E. Goldberg, Russell Korte, D. Goldberg, 2010 .

Toward a Control Map for Niching

David E. Goldberg, Jeffrey Horn, D. Goldberg, 1998, FOGA.

Comparative Study of Genetic Algorithm Performed in a Single Generation for two Different Fitness Functions Technique f(x) = x^2 and f(x) = x^2+1

D. Goldberg, K. Sastry, Manju Sharma, 2016 .

The Race, the Hurdle, and the Sweet Spot

David E. Goldberg, D. Goldberg, 1998 .

Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation

David E. Goldberg, L. Darrell Whitley, Lee Spector, 2000 .

Genetic programming for multitimescale modeling

D. Goldberg, K. Sastry, Duane D. Johnson, 2004, cond-mat/0405415.

Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2009, Evolutionary Computation.

Improving small population performance under noise with viral infection + tropism

David E. Goldberg, Kumara Sastry, Yuji Sato, 2008, GECCO '08.

Empirical analysis of ideal recombination on random decomposable problems

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2007, GECCO '07.

Population sizing for entropy-based model building in discrete estimation of distribution algorithms

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2007, GECCO '07.

Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and the impact on computational complexity

Martin V. Butz, David E. Goldberg, Kumara Sastry, 2007, Genetic Programming and Evolvable Machines.

Evaluation relaxation using substructural information and linear estimation

David E. Goldberg, Kumara Sastry, Cláudio F. Lima, 2006, GECCO '06.

Efficiency Enhancement of Estimation of Distribution Algorithms

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2006, Scalable Optimization via Probabilistic Modeling.

Sub-structural niching in estimation of distribution algorithms

Hussein A. Abbass, David E. Goldberg, Kumara Sastry, 2005, GECCO '05.

Convergence Time for the Linkage Learning Genetic Algorithm

David E. Goldberg, Ying-Ping Chen, D. Goldberg, 2004, Evolutionary Computation.

Sufficient conditions for deceptive and easy binary functions

Kalyanmoy Deb, David E. Goldberg, D. Goldberg, 1994, Annals of Mathematics and Artificial Intelligence.

Construction of high-order deceptive functions using low-order Walsh coefficients

David E. Goldberg, D. Goldberg, 1992, Annals of Mathematics and Artificial Intelligence.

Introducing Subchromosome Representations to the Linkage Learning Genetic Algorithm

David E. Goldberg, Ying-Ping Chen, D. Goldberg, 2004, GECCO.

Efficiency enhancement of genetic algorithms via building-block-wise fitness estimation

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

Designing Competent Mutation Operators Via Probabilistic Model Building of Neighborhoods

David E. Goldberg, Kumara Sastry, D. Goldberg, 2004, GECCO.

Let's Get Ready to Rumble: Crossover Versus Mutation Head to Head

David E. Goldberg, Kumara Sastry, D. Goldberg, 2004, GECCO.

An analysis of a reordering operator on a GA-hard problem

D. E. Goldberg, C. L. Bridges, D. Goldberg, 1990, Biological Cybernetics.

BUILDING-BLOCK SUPPLY IN GENETIC PROGRAMMING

David E. Goldberg, David J. Hill, Kumara Sastry, 2003 .

Discovering Deep Building Blocks for Competent Genetic Algorithms Using Chance Discovery via KeyGraphs

David E. Goldberg, Kumara Sastry, Yukio Ohsawa, 2003, Chance Discovery.

Large-Scale Permutation Optimization with the Ordering Messy Genetic Algorithm

David E. Goldberg, Dimitri Knjazew, D. Goldberg, 2000, PPSN.

The compact genetic algorithm

David E. Goldberg, Fernando G. Lobo, Georges R. Harik, 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

Parallel Recombinative Simulated Annealing: A Genetic Algorithm

David E. Goldberg, Samir W. Mahfoud, D. Goldberg, 1995, Parallel Comput..

Genetic Algorithm Di culty and the Modality ofFitness

D. Goldberg, 1994 .

Multimodal Deceptive Functions

Kalyanmoy Deb, David E. Goldberg, Jeffrey Horn, 1993, Complex Syst..

SIGNAL TIMING DETERMINATION USING GENETIC ALGORITHMS

David E. Goldberg, Rahim F Benekohal, Mark D Foy, 1992 .

Genetic Algorithms as a Computational Theory of Conceptual Design

D. E. Goldberg, D. Goldberg, 1991 .

The Nonuniform Walsh-Schema Transform

David E. Goldberg, Clayton L. Bridges, D. Goldberg, 1990, FOGA.

Messy Genetic Algorithms Revisited: Studies in Mixed Size and Scale

Kalyanmoy Deb, David E. Goldberg, Bradley Korb, 1990, Complex Syst..

Combining competent crossover and mutation operators: a probabilistic model building approach

David E. Goldberg, Kumara Sastry, Cláudio F. Lima, 2005, GECCO '05.

Control system optimization using genetic algorithms

David E. Goldberg, K. Krishnakumar, D. Goldberg, 1992 .

Key Elements Extraction in Online Collaborative Environments

Xavier Llorà, David E. Goldberg, Hiroshi Tamura, 2007, ICEIS.

Long Path Problems

Kalyanmoy Deb, David E. Goldberg, Jeffrey Horn, 1994, PPSN.

Facetwise Analysis of XCS for Problems With Class Imbalances

David E. Goldberg, Kumara Sastry, Pier Luca Lanzi, 2009, IEEE Transactions on Evolutionary Computation.

Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding

David E. Goldberg, D. Goldberg, 1990, Machine Learning.

Probability matching, the magnitude of reinforcement, and classifier system bidding

David E. Goldberg, D. Goldberg, 2004, Machine Learning.

Variable Default Hierarchy Separation in a Classifier System

David E. Goldberg, Robert E. Smith, D. Goldberg, 1990, FOGA.

Linkage Learning Accuracy in the Bayesian Optimization Algorithm

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2008, Linkage in Evolutionary Computation.

Empirical analysis of generalization and learning in XCS with gradient descent

Martin V. Butz, David E. Goldberg, Pier Luca Lanzi, 2007, GECCO '07.

Automated Global Structure Extraction for Effective Local Building Block Processing in XCS

Martin V. Butz, Xavier Llorà, David E. Goldberg, 2006, Evolutionary Computation.

Fluctuating Crosstalk as a Source of Deterministic Noise and Its Effects on GA Scalability

David E. Goldberg, Kumara Sastry, Cláudio F. Lima, 2006, EvoWorkshops.

Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness

Xavier Llorà, David E. Goldberg, Kumara Sastry, 2005, GECCO '05.

Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination

David E. Goldberg, Kumara Sastry, Tian-Li Yu, 2005, GECCO '05.

Quality-time analysis of multi-objective evolutionary algorithms

David E. Goldberg, Shinn-Ying Ho, Jian-Hung Chen, 2005, GECCO '05.

Redundant Representations in Evolutionary Computation

Franz Rothlauf, David E. Goldberg, D. Goldberg, 2003, Evolutionary Computation.

Scalability of Selectorecombinative Genetic Algorithms for Problems with Tight Linkage

David E. Goldberg, Kumara Sastry, D. Goldberg, 2003, GECCO.

Optimal Sampling and Speed-Up for Genetic Algorithms on the Sampled OneMax Problem

David E. Goldberg, Kumara Sastry, Tian-Li Yu, 2003, GECCO.

Are Multiple Runs of Genetic Algorithms Better than One?

David E. Goldberg, Erick Cantú-Paz, D. Goldberg, 2003, GECCO.

From Twomax To The Ising Model: Easy And Hard Symmetrical Problems

David E. Goldberg, Bart Naudts, Clarissa Van Hoyweghen, 2002, GECCO.

Network Random KeysA Tree Representation Scheme for Genetic and Evolutionary Algorithms

Franz Rothlauf, David E. Goldberg, Armin Heinzl, 2002, Evolutionary Computation.

On the Scalability of Parallel Genetic Algorithms

David E. Goldberg, Erick Cantú-Paz, D. Goldberg, 1999, Evolutionary Computation.

An information theoretic method for developing modular architectures using genetic algorithms

David E. Goldberg, Tian-Li Yu, Ali A. Yassine, 2007 .

Real-coded Bayesian Optimization Algorithm

David E. Goldberg, Chang Wook Ahn, R. S. Ramakrishna, 2006, Towards a New Evolutionary Computation.

Bounding the Population Size in XCS to Ensure Reproductive Opportunities

Martin V. Butz, David E. Goldberg, D. Goldberg, 2003, GECCO.

The Design of Innovation: Lessons from and for Competent Genetic Algorithms

David E. Goldberg, D. Goldberg, 2002 .

Introducing a Genetic Generalization Pressure to the Anticipatory Classiier System Part 2: Performance Analysis Introducing a Genetic Generalization Pressure to the Anticipatory Classiier System Part 2: Performance Analysis

David E. Goldberg, Wolfgang Stolzmann, D. Goldberg, 2007 .

Rule-Based Evolutionary Online Learning Systems: Learning Bounds, Classification, and Prediction

Martin V. Butz, David E. Goldberg, D. Goldberg, 2004 .

Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System - Part 1: Theoretical approach

Martin V. Butz, David E. Goldberg, Wolfgang Stolzmann, 2000, GECCO.

Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System Part 2: Performa

D. Goldberg, Martin Volker Butz, W. Stolzmann, 2000 .

Performance of Evolutionary Algorithms on Random Decomposable Problems

Martin V. Butz, David E. Goldberg, Martin Pelikan, 2006, PPSN.

Effect of Pure Error-Based Fitness in XCS

Martin V. Butz, David E. Goldberg, Pier Luca Lanzi, 2005, IWLCS.

Genetic Algorithms and the Variance of Fitness

David E. Goldberg, Mike Rudnick, D. Goldberg, 1991, Complex Syst..

Model accuracy in the Bayesian optimization algorithm

David E. Goldberg, Martin Pelikan, Cláudio F. Lima, 2011, Soft Comput..

Oiling the Wheels of Change: The Role of Adaptive Automatic Problem Decomposition in Non-Stationary Environments pdf

Hussein A. Abbass, David E. Goldberg, Kumara Sastry, 2005, ArXiv.

Sub-structural Niching in Non-stationary Environments

Hussein A. Abbass, David E. Goldberg, Kumara Sastry, 2004, Australian Conference on Artificial Intelligence.

Genetic Algorithms, Tournament Selection, and the Effects of Noise

David E. Goldberg, Brad L. Miller, D. Goldberg, 1995, Complex Syst..

Computer-aided pipeline operation using genetic algorithms and rule learning. PART I: Genetic algorithms in pipeline optimization

David E. Goldberg, D. Goldberg, 1987, Engineering with Computers.

Decision making in a hybrid genetic algorithm

David E. Goldberg, Fernando G. Lobo, D. Goldberg, 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

Search space boundary extension method in real-coded genetic algorithms

David E. Goldberg, Shigeyoshi Tsutsui, D. Goldberg, 2001, Inf. Sci..

OMEGA - Ordering Messy GA: Solving Permutation Problems with the Fast Genetic Algorithm and Random Keys

David E. Goldberg, Dimitri Knjazew, D. Goldberg, 2000, GECCO.

Genetic algorithm design of Pareto optimal broadband microwave absorbers

David E. Goldberg, Eric Michielssen, Daniel S. Weile, 1996 .

Philosophy and Engineering: Reflections on Practice, Principles and Process

David E. Goldberg, Diane P. Michelfelder, Natasha McCarthy, 2016, IEEE Transactions on Education.

Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise

David E. Goldberg, Brad L. Miller, D. Goldberg, 1996, Evolutionary Computation.

Efficient Parallel Genetic Algorithms: Theory and Practice

David E. Goldberg, Erick Cantú-Paz, D. Goldberg, 2000 .

Design of microvascular flow networks using multi-objective genetic algorithms

David E. Goldberg, Philippe H. Geubelle, Scott R. White, 2008 .

The Crowding Approach to Niching in Genetic Algorithms

Ole J Mengshoel, David E Goldberg, D. Goldberg, 2008, Evolutionary Computation.

Adaptive Hybrid Genetic Algorithm for Groundwater Remediation Design

David E. Goldberg, Barbara S. Minsker, Felipe P. Espinoza, 2005 .

The Everyday Engineering of Organizational and Engineering Innovation

David E. Goldberg, D. Goldberg, 2004 .

Simplifying multiobjective optimization: An automated design methodology for the nondominated sorted genetic algorithm‐II

David E. Goldberg, Patrick M. Reed, Barbara S. Minsker, 2003 .

SIMPLIFYING MULTIOBJECTIVE OPTIMIZATION USING GENETIC ALGORITHMS

David E. Goldberg, Patrick M. Reed, Barbara S. Minsker, 2003 .

The Practitioner's Role in Competent Search and Optimization Using Genetic Algorithms

David E. Goldberg, Patrick M. Reed, Barbara S. Minsker, 2001 .

Designing a competent simple genetic algorithm for search and optimization

David E. Goldberg, Patrick M. Reed, Barbara S. Minsker, 2000 .

A hydroinformatician's approach to computational innovation and the design of genetic algorithms

David E. Goldberg, D. Goldberg, 2000 .

The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations

E. Cantu-Paz, D.E. Goldberg, B.L. Miller, 1997, Evolutionary Computation.

3 Lessons of Genetic Algorithms for Computational Innovation

David E. Goldberg, D. Goldberg, 1999 .

The Genetic Algorithm Approach: Why, How, and What Next?

David E. Goldberg, D. Goldberg, 1986 .

The parameter-less genetic algorithm in practice

David E. Goldberg, Fernando G. Lobo, D. Goldberg, 2004, Inf. Sci..

Fitness Inheritance In Multi-objective Optimization

David E. Goldberg, Kumara Sastry, Shinn-Ying Ho, 2002, GECCO.

Genetic Programming for Multiscale Modeling

David E. Goldberg, Kumara Sastry, Pascal Bellon, 2004 .

A Genetic Algorithm for Developing Modular Product Architectures

David E. Goldberg, Tian-Li Yu, Ali A. Yassine, 2003 .

Loopy Substructural Local Search for the Bayesian Optimization Algorithm

David E. Goldberg, Martin Pelikan, Cláudio F. Lima, 2009, SLS.

Where Does the Good Stuff Go, and Why? How Contextual Semantics Influences Program Structure in Simple Genetic Programming

David E. Goldberg, Una-May O'Reilly, D. Goldberg, 1998, EuroGP.

Hierarchical Bayesian Optimization Algorithm

David E. Goldberg, Martin Pelikan, D. Goldberg, 2006, Scalable Optimization via Probabilistic Modeling.

Let's get ready to rumble redux: crossover versus mutation head to head on exponentially scaled problems

David E. Goldberg, Kumara Sastry, D. Goldberg, 2007, GECCO '07.

Hierarchical Problem Solving and the Bayesian Optimization Algorithm

David E. Goldberg, Martin Pelikan, D. Goldberg, 2000, GECCO.

SEARCH, Blackbox Optimization, And Sample Complexity

David E. Goldberg, Hillol Kargupta, D. Goldberg, 1996, FOGA.

Genetic Algorithms in Pipeline Optimization

David E. Goldberg, Chie Hsiung Kuo, D. Goldberg, 1987 .

Genetic and evolutionary algorithms come of age

David E. Goldberg, D. Goldberg, 1994, CACM.

Efficient Genetic Algorithms Using Discretization Scheduling

David E. Goldberg, Laura A. McLay, D. Goldberg, 2005, Evolutionary Computation.

Optimizing Global-Local Search Hybrids

David E. Goldberg, Siegfried Vössner, D. Goldberg, 1999, GECCO.

Graph-theoretic measure for active iGAs: interaction sizing and parallel evaluation ensemble

Xavier Llorà, David E. Goldberg, Noriko Imafuji Yasui, 2008, GECCO '08.

Bad Codings and the Utility of Well-Designed Genetic Algorithms

Franz Rothlauf, David E. Goldberg, Armin Heinzl, 2000, GECCO.

Risk‐based in situ bioremediation design using a noisy genetic algorithm

David E. Goldberg, Barbara S. Minsker, J. Bryan Smalley, 2000 .

Optimal sampling in a noisy genetic algorithm for risk-based remediation design

David E. Goldberg, Barbara S. Minsker, Gayathri Gopalakrishnan, 2001 .

Optimization and Machine Learning

D. E. Goldberg, D. Goldberg, 2022 .

PAC Learning in XCS

Martin V. Butz, David E. Goldberg, Pier Luca Lanzi, 2004 .

Communication Gap Management for Fertile Community

Xavier Llorà, David E. Goldberg, Naohiro Matsumura, 2007, Soft Comput..

Communication Gaps in Social Networks

Xavier Llorà, David E. Goldberg, Naohiro Matsumura, 2005, WSTST.

Mining Social Networks in Message Boards

David E. Goldberg, Naohiro Matsumura, Xavier Llor, 2005 .

Speeding online synthesis via enforced selecto-recombination

Xavier Llorà, David E. Goldberg, Kumara Sastry, 2008, GECCO '08.

First Cognitive Capabilities in the Anticipatory Classifier System

D. Goldberg, Martin Volker Butz, J. Hoffmann, 2000 .

Simplex crossover and linkage identification: single-stage evolution vs. multi-stage evolution

David E. Goldberg, Shigeyoshi Tsutsui, D. Goldberg, 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

Substructural Neighborhoods for Local Search in the Bayesian Optimization Algorithm

Martin V. Butz, David E. Goldberg, Martin Pelikan, 2006, PPSN.

Multi-objective bayesian optimization algorithm

David E. Goldberg, Martin Pelikan, Nazan Khan, 2002 .

Genetic Algorithms, Clustering, and the Breaking of Symmetry

David E. Goldberg, Martin Pelikan, D. Goldberg, 2000, PPSN.

The innovation pump: supporting creative processes in collaborative engineering

Xavier Llorà, David E. Goldberg, D. Goldberg, 2009 .

Analyzing active interactive genetic algorithms using visual analytics

Xavier Llorà, David E. Goldberg, Kumara Sastry, 2006, GECCO '06.

Efficient Interactive Weight Tuning For Tts Synthesis: Reducing User Fatigue By Improving User Consistency

Xavier Llorà, David E. Goldberg, Kumara Sastry, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Evaluation Consistency in iGAs: User Contradictions as Cycles in Partial-Ordering Graphs

David E. Goldberg, Kumara Sastry, Xavier Llorà, 2005 .

Online population size adjusting using noise and substructural measurements

David E. Goldberg, Kumara Sastry, Tian-Li Yu, 2005, 2005 IEEE Congress on Evolutionary Computation.

SGA-C: A C-language Implementation of a Simple Genetic Algorithm

David E. Goldberg, E. Robert, Jeff A. Earickson, 1991 .

Boolean Function Learning With A Classifier System

Abdollah Homaifar, C. C. Carroll, D. E. Goldberg, 1988, Defense, Security, and Sensing.

Scaling Genetic Algorithms Using MapReduce

Xavier Llorà, David E. Goldberg, Roy H. Campbell, 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

Wise Breeding GA via Machine Learning Techniques for Function Optimization

Xavier Llorà, David E. Goldberg, D. Goldberg, 2003, GECCO.

Creating Scalable Reform in Engineering Education Through Low-Cost In- trinsic Motivation Course Conversions of Engineering Courses

David E. Goldberg, Michael C. Loui, Geoffrey L. Herman, 2013 .

Why Philosophy? Why Now? Engineering Responds to the Crisis of a Creative Era

David E. Goldberg, D. Goldberg, 2009 .

Real-Coded Extended Compact Genetic Algorithm Based on Mixtures of Models

David E. Goldberg, Kumara Sastry, Pier Luca Lanzi, 2008, Linkage in Evolutionary Computation.

A Simple Real-Coded Extended Compact Genetic Algorithm

David E. Goldberg, Kumara Sastry, Pier Luca Lanzi, 2007, 2007 IEEE Congress on Evolutionary Computation.

Meandre: Semantic-Driven Data-Intensive Flows in the Clouds

Xavier Llorà, David E. Goldberg, Loretta Auvil, 2008, 2008 IEEE Fourth International Conference on eScience.

INNOVATION AND CREATIVITY SUPPORT VIA CHANCE DISCOVERY, GENETIC ALGORITHMS, AND DATA MINING

Xavier Llorà, David E. Goldberg, Loretta Auvil, 2006 .

Chances and Marketing: On-line Conversation Analysis for Creative Scenario Discussion

David E. Goldberg, Yukio Ohsawa, Hiroshi Tamura, 2004 .

Combining Reliability and Pareto Optimality—An Approach Using Stochastic Multi-Objective Genetic Algorithms

David E. Goldberg, Abhishek Singh, Barbara S. Minsker, 2003 .

Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm

David E. Goldberg, Tian-Li Yu, Ali Yassine, 2003, GECCO.

Linkage Identification by Non-monotonicity Detection for Overlapping Functions

David E. Goldberg, Masaharu Munetomo, D. Goldberg, 1999, Evolutionary Computation.

Evolutionary Computation As A Form Of Organization

David E. Goldberg, Alexander Kosorukoff, D. Goldberg, 2002, GECCO.

Using Previous Models to Bias Structural Learning in the Hierarchical BOA

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2008, Evolutionary Computation.

Military antenna design using simple and competent genetic algorithms

David E. Goldberg, Scott Santarelli, Tian-Li Yu, 2006, Math. Comput. Model..

Sporadic model building for efficiency enhancement of hierarchical BOA

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2006, GECCO '06.

Polynomial complexity blackbox search: lessons from the SEARCH framework

David E. Goldberg, Hillol Kargupta, D. Goldberg, 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

Multiobjective hBOA, clustering, and scalability

David E. Goldberg, Martin Pelikan, Kumara Sastry, 2005, GECCO '05.

Scalability of a Hybrid Extended Compact Genetic Algorithm for Ground State Optimization of Clusters

David E. Goldberg, Kumara Sastry, Duane D. Johnson, 2007 .

A hierarchy machine: Learning to optimize from nature and humans

David E. Goldberg, Martin Pelikan, D. Goldberg, 2003, Complex..

Analysis and evaluation of learning classifier systems applied to hyperspectral image classification

Martin V. Butz, David E. Goldberg, Arnaud Quirin, 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

Time Complexity of genetic algorithms on exponentially scaled problems

David E. Goldberg, Martin Pelikan, Fernando G. Lobo, 2000, GECCO.

The gambler''s ruin problem

David E. Goldberg, Erick Cantú-Paz, Brad L. Miller, 1997 .

Hierarchical BOA Solves Ising Spin Glasses and MAXSAT

David E. Goldberg, Martin Pelikan, D. Goldberg, 2003, GECCO.

Resource management and scalability of the XCSF learning classifier system

Martin V. Butz, Xavier Llorà, David E. Goldberg, 2012, Theor. Comput. Sci..

Experience-Based Creativity

A. Barto, R. Yee, J. Holland, 1991 .

A Whole New Engineer

David E. Goldberg, Mark Somerville, D. Goldberg, 2014 .

Inverse kinematics of redundant robots using genetic algorithms

David E. Goldberg, Joey K. Parker, Ahmad R. Khoogar, 1989, Proceedings, 1989 International Conference on Robotics and Automation.

Multiple-Deme Parallel Estimation of Distribution Algorithms: Basic Framework and Application

David E. Goldberg, Chang Wook Ahn, R. S. Ramakrishna, 2003, PPAM.

Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World

David E. Goldberg, Chang Wook Ahn, R. S. Ramakrishna, 2004, GECCO.

Fluctuating crosstalk, deterministic noise, and GA scalability

David E. Goldberg, Paul Winward, D. Goldberg, 2006, GECCO.

Binary Representation in Gene Expression Programming: Towards a Better Scalability

Jose Garcia Moreno-Torres, Xavier Llorà, David E. Goldberg, 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

Linkage Learning, Rule Representation, and the X-Ary Extended Compact Classifier System

Xavier Llorà, David E. Goldberg, Kumara Sastry, 2008, IWLCS.

Sporadic model building for efficiency enhancement of the hierarchical BOA

D. Goldberg, M. Pelikán, K. Sastry, 2008, Genetic Programming and Evolvable Machines.

Evolutionary Algorithm for Large Scale Problems

David E. Goldberg, Kumara Sastry, Alexandre C. B. Delbem, 2007, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007).

Optimization of Semiempirical Quantum Chemistry Methods via Multiobjective Genetic Algorithms: Accurate Photodynamics for Larger Molecules and Longer Time Scales

David E. Goldberg, Kumara Sastry, A. L. Thompson, 2007 .

Extending XCSF beyond linear approximation

Daniele Loiacono, David E. Goldberg, Stewart W. Wilson, 2005, GECCO '05.

FOX-GA: A Genetic Algorithm for Generating and Analyzing Battlefield Courses of Action

David E. Goldberg, C. C. Hayes, J. L. Schlabach, 1999, Evolutionary Computation.

Substructrual surrogates for learning decomposable classification problems: implementation and first results

David E. Goldberg, Kumara Sastry, Ester Bernadó-Mansilla, 2007, GECCO '07.

Combining The Strengths Of Bayesian Optimization Algorithm And Adaptive Evolution Strategies

David E. Goldberg, Martin Pelikan, Shigeyoshi Tsutsui, 2002, GECCO.

Adaptive hydrogenerator governor tuning with a genetic algorithm

L. Wozniak, D. E. Goldberg, J. E. Lansberry, 1992 .