Adaptive Evolutionary Algorithms on Unitation, Royal Road and Longpath Functions

Genetic algorithms (GAs) are powerful tools that allow engineers and scientists to find good solutions to hard computational problems using evolutionary principles. The classic genetic algorithm suffers from the configuration problem, the difficulty of choosing optimal parameter settings. Genetic algorithm literature is full of empirical tricks, techniques, and rules of thumb that enable GAs to be optimized to perform better in some way by altering the GA parameters. However these techniques are often analyzed on only a narrow set of fitness functions. This paper is a first empirical step in analyzing several parameter adaptive techniques on the unitation class of fitness functions, where fitness is a function of the number of ones in the binary genome.

[1]  D. Thierens.  Adaptive mutation rate control schemes in genetic algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[2]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[3]  Kalyanmoy Deb,et al.  Long Path Problems , 1994, PPSN.

[4]  James R. Neil,et al.  Analysis of the simple genetic algorithm on the single-peak and double-peak landscapes , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[5]  Alden H. Wright,et al.  EA models and population fixed-points versus mutation rates for functions of unitation , 2005, GECCO '05.

[6]  Colin R. Reeves,et al.  Genetic Algorithms—Principles and Perspectives , 2002, Operations Research/Computer Science Interfaces Series.

[7]  Melanie Mitchell,et al.  The royal road for genetic algorithms: Fitness landscapes and GA performance , 1991 .

[8]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[9]  Thomas Bäck,et al.  Intelligent Mutation Rate Control in Canonical Genetic Algorithms , 1996, ISMIS.

[10]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[11]  Thomas Jansen,et al.  Dynamic Parameter Control in Simple Evolutionary Algorithms , 2001, FOGA.

[12]  Russell C. Eberhart,et al.  Implementation of evolutionary fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..

[13]  Hans-Georg Beyer,et al.  The Theory of Evolution Strategies , 2001, Natural Computing Series.

[14]  Zbigniew Michalewicz,et al.  Adaptation in evolutionary computation: a survey , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[15]  Jonathan E. Rowe,et al.  Population Fixed-Points for Functions of Unitation , 1998, FOGA.