Knowledge Incorporation in Multi-objective Evolutionary Algorithms

This chapter presents a survey of techniques used to incorporate knowledge into evolutionary algorithms, with a particular emphasis on multi-objective optimization. We focus on two main groups of techniques: those that incorporate knowledge into the fitness evaluation, and those that incorporate knowledge in the initialization process and the operators of an evolutionary algorithm. Several techniques representative of each of these groups are briefly discussed, together with some examples found in the specialized literature. In the last part of the chapter, we provide some research ideas that are worth exploring in the future by researchers interested in this topic.

[1]  Tapabrata Ray,et al.  Performance of kriging and cokriging based surrogate models within the unified framework for surrogate assisted optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[2]  A. Keane,et al.  Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .

[3]  Kok Wai Wong,et al.  Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems , 2005 .

[4]  David E. Goldberg,et al.  Fitness Inheritance In Multi-objective Optimization , 2002, GECCO.

[5]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[6]  Carlos A. Coello Coello,et al.  Solving Hard Multiobjective Optimization Problems Using epsilon-Constraint with Cultured Differential Evolution , 2006, PPSN.

[7]  Sushil J. Louis,et al.  Use of case injection to bias genetic algorithm solutions of similar problems , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[8]  C. Coello,et al.  Cultured differential evolution for constrained optimization , 2006 .

[9]  Carlos A. Coello Coello,et al.  Evolutionary multiobjective optimization using a cultural algorithm , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[10]  Khaled Rasheed,et al.  Comparison of methods for developing dynamic reduced models for design optimization , 2002, Soft Comput..

[11]  Kalyanmoy Deb,et al.  Evaluating the -Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions , 2005, Evolutionary Computation.

[12]  Sushil J. Louis,et al.  Solving Similar Problems Using Genetic Algorithms and Case-Based Memory , 1997, ICGA.

[13]  Marios K. Karakasis,et al.  METAMODEL-ASSISTED MULTI-OBJECTIVE EVOLUTIONARY OPTIMIZATION , 2005 .

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  Joshua D. Knowles,et al.  ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems , 2006, IEEE Transactions on Evolutionary Computation.

[16]  Liang Shi,et al.  Multiobjective GA optimization using reduced models , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[17]  R. Lyndon While,et al.  A review of multiobjective test problems and a scalable test problem toolkit , 2006, IEEE Transactions on Evolutionary Computation.

[18]  Mikkel T. Jensen,et al.  Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms , 2003, IEEE Trans. Evol. Comput..

[19]  Kalyanmoy Deb,et al.  Towards a Quick Computation of Well-Spread Pareto-Optimal Solutions , 2003, EMO.

[20]  Sushil J. Louis,et al.  Case-Injection Improves Response Time for a Real-Time Strategy Game , 2005, CIG.

[21]  Robert G. Reynolds,et al.  Knowledge-based self-adaptation in evolutionary programming using cultural algorithms , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[22]  Mehrdad Salami,et al.  A fast evaluation strategy for evolutionary algorithms , 2003, Appl. Soft Comput..

[23]  Lakhmi C. Jain,et al.  Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[24]  Bernhard Sendhoff,et al.  Structure optimization of neural networks for evolutionary design optimization , 2005, Soft Comput..

[25]  T. W. Layne,et al.  A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models , 1998 .

[26]  Robert E. Smith,et al.  Fitness inheritance in genetic algorithms , 1995, SAC '95.

[27]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[28]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[29]  Hussein A. Abbass,et al.  Fitness inheritance for noisy evolutionary multi-objective optimization , 2005, GECCO '05.

[30]  Carlos A. Coello Coello,et al.  Optimization with constraints using a cultured differential evolution approach , 2005, GECCO '05.

[31]  Carlos A. Coello Coello,et al.  Adding Knowledge And Efficient Data Structures To Evolutionary Programming: A Cultural Algorithm For Constrained Optimization , 2002, GECCO.

[32]  Stephane Pierret,et al.  Turbomachinery Blade Design Using a Navier–Stokes Solver and Artificial Neural Network , 1999 .

[33]  Petros Koumoutsakos,et al.  Accelerating evolutionary algorithms with Gaussian process fitness function models , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[34]  Zbigniew Michalewicz,et al.  Using Cultural Algorithms for Constraint Handling in GENOCOP , 1995, Evolutionary Programming.

[35]  Robert G. Reynolds,et al.  Cultural algorithms in dynamic environments , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[36]  Sushil J. Louis,et al.  A sequential similarity metric for case injected genetic algorithms applied to TSPs , 1999 .

[37]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[38]  Robert G. Reynolds,et al.  CAEP: An Evolution-Based Tool for Real-Valued Function Optimization Using Cultural Algorithms , 1998, Int. J. Artif. Intell. Tools.

[39]  Sushil J. Louis,et al.  Case-based reasoning assisted explanation of genetic algorithm results , 1993, J. Exp. Theor. Artif. Intell..

[40]  Yaochu Jin,et al.  A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..

[41]  Carlos A. Coello Coello,et al.  A study of fitness inheritance and approximation techniques for multi-objective particle swarm optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[42]  Marc Schoenauer,et al.  Surrogate Deterministic Mutation: Preliminary Results , 2001, Artificial Evolution.

[43]  R. Reynolds,et al.  Using knowledge-based evolutionary computation to solve nonlinear constraint optimization problems: a cultural algorithm approach , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[44]  B. Julstrom,et al.  Design of vector quantization codebooks using a genetic algorithm , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[45]  Min-Jea Tahk,et al.  Acceleration of the convergence speed of evolutionary algorithms using multi-layer neural networks , 2003 .

[46]  Bernard De Baets,et al.  Is Fitness Inheritance Useful for Real-World Applications? , 2003, EMO.

[47]  Sushil J. Louis,et al.  Genetic learning for combinational logic design , 2005, Soft Comput..

[48]  Kalyanmoy Deb,et al.  Computationally effective search and optimization procedure using coarse to fine approximations , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[49]  Alain Ratle,et al.  Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation , 1998, PPSN.

[50]  Andreas Zell,et al.  Evolution strategies assisted by Gaussian processes with improved preselection criterion , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[51]  Kevin Tucker,et al.  Response surface approximation of pareto optimal front in multi-objective optimization , 2004 .

[52]  Thomas Bäck,et al.  Metamodel-Assisted Evolution Strategies , 2002, PPSN.

[53]  R. Lyndon While,et al.  A Scalable Multi-objective Test Problem Toolkit , 2005, EMO.

[54]  J. J. Brewster,et al.  Cultural swarms , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[55]  Robert G. Reynolds,et al.  A Cultural Algorithm Framework to Evolve Multi-Agent Cooperation with Evolutionary Programming , 1997, Evolutionary Programming.

[56]  T. Simpson,et al.  Efficient Pareto Frontier Exploration using Surrogate Approximations , 2000 .

[57]  Carlos A. Coello Coello,et al.  Extraction and reuse of design patterns from genetic algorithms using case-based reasoning , 2005, Soft Comput..

[58]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[59]  Andy J. Keane,et al.  Multi-Objective Optimization Using Surrogates , 2010 .

[60]  P. Di Barba,et al.  Combining response surfaces and evolutionary strategies for multiobjective pareto-optimization in electromagnetics , 2002 .

[61]  Andreas Zell,et al.  Model-Assisted Steady-State Evolution Strategies , 2003, GECCO.

[62]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[63]  R. L. Hardy Multiquadric equations of topography and other irregular surfaces , 1971 .

[64]  M. Farina A neural network based generalized response surface multiobjective evolutionary algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[65]  Carlos A. Coello Coello,et al.  Dynamic fitness inheritance proportion for multi-objective particle swarm optimization , 2006, GECCO.

[66]  Michael T. M. Emmerich,et al.  Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels , 2006, IEEE Transactions on Evolutionary Computation.

[67]  D. Goldberg,et al.  Don't evaluate, inherit , 2001 .

[68]  Christopher K. Riesbeck,et al.  Inside Case-Based Reasoning , 1989 .

[69]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.