Evolving black-box search algorithms employing genetic programming

Restricting the class of problems we want to perform well on allows Black Box Search Algorithms (BBSAs) specifically tailored to that class to significantly outperform more general purpose problem solvers. However, the fields that encompass BBSAs, including Evolutionary Computing, are mostly focused on improving algorithm performance over increasingly diversified problem classes. By definition, the payoff for designing a high quality general purpose solver is far larger in terms of the number of problems it can address, than a specialized BBSA. This paper introduces a novel approach to creating tailored BBSAs through automated design employing genetic programming. An experiment is reported which demonstrates its ability to create novel BBSAs which outperform established BBSAs including canonical evolutionary algorithms.

[1]  Daniel R. Tauritz,et al.  Self-configuring crossover , 2011, GECCO.

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

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

[4]  Mihai Oltean,et al.  Evolving evolutionary algorithms using evolutionary algorithms , 2007, GECCO '07.

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

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

[7]  Mihai Oltean,et al.  Evolving Evolutionary Algorithms Using Multi Expression Programming , 2003, ECAL.

[8]  Jerry Swan,et al.  Automatically designing selection heuristics , 2011, GECCO.

[9]  Bruce Edmonds,et al.  Meta-Genetic Programming: Co-evolving the Operators of Variation , 2001 .

[10]  Jerry Swan,et al.  The automatic generation of mutation operators for genetic algorithms , 2012, GECCO '12.

[11]  Mihai Oltean,et al.  Evolving Evolutionary Algorithms Using Linear Genetic Programming , 2005, Evolutionary Computation.

[12]  Peter J. Angeline,et al.  Two self-adaptive crossover operators for genetic programming , 1996 .

[13]  Mihai Oltean,et al.  Evolutionary design of Evolutionary Algorithms , 2009, Genetic Programming and Evolvable Machines.

[14]  A. E. Eiben,et al.  Comparing parameter tuning methods for evolutionary algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[15]  Nuno Lourenço,et al.  Evolving evolutionary algorithms , 2012, GECCO '12.

[16]  A. E. Eiben,et al.  Diagonal Crossover in Genetic Algorithms for Numerical Optimization , 1997 .