A Dimensionally-Aware Genetic Programming Architecture for Automated Innovization

Automated innovization is an unsupervised machine learning technique for extracting useful design knowledge from Pareto-optimal solutions in the form of mathematical relationships of a certain structure. These relationships are known as design principles. Past studies have shown the applicability of automated innovization on a number of engineering design optimization problems using a multiplicative form for the design principles. In this paper, we generalize the structure of the obtained principles using a tree-based genetic programming framework. While the underlying innovization algorithm remains the same, evolving multiple trees, each representing a different design principle, is a challenging task. We also propose a method for introducing dimensionality information in the search process to produce design principles that are not just empirical in nature, but also meaningful to the user. The procedure is illustrated for three engineering design problems.

[1]  Daisuke Sasaki,et al.  Visualization and Data Mining of Pareto Solutions Using Self-Organizing Map , 2003, EMO.

[2]  Ken E. Whelan,et al.  The Automation of Science , 2009, Science.

[3]  Aravind Srinivasan,et al.  Innovization: innovating design principles through optimization , 2006, GECCO.

[4]  Kalyanmoy Deb,et al.  Hybrid Evolutionary Multi-Objective Optimization of Machining Parameters , 2011 .

[5]  Kalyanmoy Deb,et al.  Hybrid evolutionary multi-objective optimization and analysis of machining operations , 2012 .

[6]  Kalyanmoy Deb,et al.  Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique , 2011 .

[7]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[8]  M. Newman Power laws, Pareto distributions and Zipf's law , 2004, cond-mat/0412004.

[9]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[10]  David W. Coit,et al.  Data mining techniques to facilitate the analysis of the pareto-optimal set for multiple objective problems , 2006 .

[11]  M. Keijzer,et al.  Dimensionally aware genetic programming , 1999 .

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

[13]  Peter Nijkamp,et al.  Accessibility of Cities in the Digital Economy , 2013 .

[14]  Raúl E. Valdés-Pérez,et al.  Principles of Human Computer Collaboration for Knowledge Discovery in Science , 1999, Artif. Intell..

[15]  Jonathan E. Fieldsend,et al.  Visualisation and ordering of many-objective populations , 2010, IEEE Congress on Evolutionary Computation.

[16]  Riccardo Poli,et al.  Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques and Applications , 2008, Computational Intelligence: A Compendium.

[17]  Peter J. Angeline,et al.  On Using Syntactic Constraints with Genetic Programming , 1996 .

[18]  Sanaz Mostaghim,et al.  Heatmap Visualization of Population Based Multi Objective Algorithms , 2007, EMO.

[19]  Kalyanmoy Deb,et al.  Automated Innovization for Simultaneous Discovery of Multiple Rules in Bi-objective Problems , 2011, EMO.

[20]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[21]  Kalyanmoy Deb,et al.  An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering , 2014, Appl. Soft Comput..

[22]  Ramón Quiza Sardiñas,et al.  Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes , 2006, Eng. Appl. Artif. Intell..

[23]  Raúl E. Valdés-Pérez Discovery tools for science apps , 1999, Commun. ACM.

[24]  Riccardo Poli,et al.  Genetic Programming An Introductory Tutorial and a Survey of Techniques and Applications , 2011 .

[25]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[26]  David J. Montana,et al.  Strongly Typed Genetic Programming , 1995, Evolutionary Computation.

[27]  Frédéric Gruau,et al.  On using syntactic constraints with genetic programming , 1996 .

[28]  Kalyanmoy Deb,et al.  A combined genetic adaptive search (GeneAS) for engineering design , 1996 .

[29]  E. Vald Principles of human-computer collaboration for knowledge discovery in science , 1999 .

[30]  Kalyanmoy Deb,et al.  An Integrated Approach to Automated Innovization for Discovering Useful Design Principles : Three Engineering Case Studies , 2012 .

[31]  Shigeru Obayashi,et al.  Multi-objective optimization and design rule mining for an aerodynamically efficient and stable centrifugal impeller with a vaned diffuser , 2010 .