The Cooperative Royal Road: Avoiding Hitchhiking

We propose using the so called Royal Road functions astest functions for cooperative co-evolutionary algorithms (CCEAs). TheRoyal Road functions were created in the early 90's with the aim ofdemonstrating the superiority of genetic algorithms over local searchmethods. Unexpectedly, the opposite was found to be true. The researchdeepened our understanding of the phenomenon of hitchhiking whereunfavorable alleles may become established in the population followingan early association with an instance of a highly fit schema. Here, wetake advantage of the modular and hierarchical structure of the RoyalRoad functions to adapt them to a co-evolutionary setting. Using a multiplepopulation approach, we show that a CCEA easily outperforms astandard genetic algorithm on the Royal Road functions, by naturallyovercoming the hitchhiking effect. Moreover, we found that the optimalnumber of sub-populations for the CCEA is not the same as the numberof components that the function can be linearly separated into, andpropose an explanation for this behavior. We argue that this class offunctions may serve in foundational studies of cooperative co-evolution.

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

[2]  Kenneth A. De Jong,et al.  The Coevolution of Antibodies for Concept Learning , 1998, PPSN.

[3]  K.A. De Jong,et al.  Analyzing cooperative coevolution with evolutionary game theory , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[4]  Colin R. Reeves,et al.  Genetic Algorithms: Principles and Perspectives: A Guide to Ga Theory , 2002 .

[5]  John H. Holland,et al.  Cognitive systems based on adaptive algorithms , 1977, SGAR.

[6]  Enrique Dunn,et al.  Parisian camera placement for vision metrology , 2006, Pattern Recognit. Lett..

[7]  Melanie Mitchell,et al.  Relative Building-Block Fitness and the Building Block Hypothesis , 1992, FOGA.

[8]  John H. Holland,et al.  COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

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

[10]  Risto Miikkulainen,et al.  Forming Neural Networks Through Efficient and Adaptive Coevolution , 1997, Evolutionary Computation.

[11]  Rudolf Paul Wiegand,et al.  An analysis of cooperative coevolutionary algorithms , 2004 .

[12]  R. Eriksson,et al.  Cooperative Coevolution in Inventory Control Optimisation , 1997, ICANNGA.

[13]  Phil Husbands,et al.  Distributed Coevolutionary Genetic Algorithms for Multi-Criteria and Multi-Constraint Optimisation , 1994, Evolutionary Computing, AISB Workshop.

[14]  John H. Holland,et al.  When will a Genetic Algorithm Outperform Hill Climbing , 1993, NIPS.

[15]  R. Paul Wiegand,et al.  An empirical analysis of collaboration methods in cooperative coevolutionary algorithms , 2001 .

[16]  Reinhard Männer,et al.  Parallel Problem Solving from Nature — PPSN III , 1994, Lecture Notes in Computer Science.

[17]  Phil Husbands,et al.  Simulated Co-Evolution as the Mechanism for Emergent Planning and Scheduling , 1991, ICGA.

[18]  Marc Schoenauer,et al.  Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving , 2000, Genetic Programming and Evolvable Machines.

[19]  Yann Landrin-Schweitzer,et al.  Introducing lateral thinking in search engines , 2006, Genetic Programming and Evolvable Machines.

[20]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[21]  J. Pollack,et al.  Hierarchically consistent test problems for genetic algorithms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[22]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

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

[24]  Kenneth A. De Jong,et al.  Understanding cooperative co-evolutionary dynamics via simple fitness landscapes , 2005, GECCO '05.

[25]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[26]  Lashon B. Booker,et al.  Proceedings of the fourth international conference on Genetic algorithms , 1991 .

[27]  S. Kauffman,et al.  Coevolution to the edge of chaos: coupled fitness landscapes, poised states, and coevolutionary avalanches. , 1991, Journal of theoretical biology.

[28]  Marie-Jeanne Lesot,et al.  Dynamic flies: a new pattern recognition tool applied to stereo sequence processing , 2002, Pattern Recognit. Lett..

[29]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.