The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems

Learnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new type of operators for creating new individuals, specifically, hypothesis generation, which learns rules indicating subareas in the search space that likely contain the optimum, and hypothesis instantiation, which populates these subspaces with new individuals. This paper briefly describes the newest and most advanced implementation of learnable evolution, LEM3, its novel features, and results from its comparison with a conventional, Darwinian-type evolutionary computation program (EA), a cultural evolution algorithm (CA), and the estimation of distribution algorithm (EDA) on selected function optimization problems (with the number of variables varying up to 1000). In every experiment, LEM3 outperformed the compared programs in terms of the evolution length (the number of fitness evaluations needed to achieved a desired solution), sometimes more than by one order of magnitude.

[1]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[2]  Pedro Larrañaga,et al.  Experimental Results in Function Optimization with EDAs in Continuous Domain , 2002, Estimation of Distribution Algorithms.

[3]  Ryszard S. Michalski,et al.  The LEM3 System for Non-Darwinian Evolutionary Computation and Its Application to Complex Function Optimization , 2005 .

[4]  Ryszard S. Michalski,et al.  Learnable Evolution: Combining Symbolic and Evolutionary Learning , 1998 .

[5]  Robert G. Reynolds,et al.  Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Piotr A. Domanski,et al.  An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model , 2004 .

[7]  L. Darrell Whitley,et al.  Evaluating Evolutionary Algorithms , 1996, Artif. Intell..

[8]  Robert G. Reynolds,et al.  Cultural algorithms: modeling of how cultures learn to solve problems , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[9]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[10]  Ryszard S. Michalski,et al.  Adaptive Anchoring Discretization for Learnable Evolution Model: The ANCHOR Method , 2001 .

[11]  Ryszard S. Michalski,et al.  LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning , 2004, Machine Learning.

[12]  Laetitia Vermeulen-Jourdan,et al.  Preliminary Investigation of the 'Learnable Evolution Model' for Faster/Better Multiobjective Water Systems Design , 2005, EMO.

[13]  Kenneth A. Kaufman,et al.  The AQ19 System for Machine Learning and Pattern Discovery: A General Description and User's Guide , 2001 .

[14]  Kenneth A. Kaufman,et al.  Recent Results from the Experimental Evaluation of the Learnable Evolution Model , 2002, GECCO Late Breaking Papers.

[15]  Janusz Wojtusiak,et al.  AQ21 USER 'S GUIDE , 2004 .

[16]  Pedro Larrañaga,et al.  Evolutionary computation based on Bayesian classifiers , 2004 .

[17]  R. Michalski.  Attributional Calculus: A Logic and Representation Language for Natural Induction , 2004 .

[18]  Janusz Wojtusiak.  THE LEM3 IMPLEMENTATION OF LEARNABLE EVOLUTION MODEL USER 'S GUIDE 1 , 2004 .