Co-evolutionary EAs are often applied to optimization and machine learning problems with disappointing results. One of the contributing factors to this is the complexity of the dynamics exhibited by co-evolutionary systems. In this paper we focus on a particular form of competitive co-evolutionary EA and study the dynamics of the fitness of the best individuals in the evolving populations. Our approach is to try to understand the characteristics of the fitness landscapes that produce particular kinds of fitness dynamics such as stable fixed points, stable cycles, and instability. In particular, we show how landscapes can be constructed that produce each of these dynamics. These landscapes are extremely similar when inspected with respect to traditional properties such as ruggedness / modality, yet they yield very different results. This shows there is a need for co-evolutionary specific analysis tools.
[1]
J. Pollack,et al.
Challenges in coevolutionary learning: arms-race dynamics, open-endedness, and medicocre stable states
,
1998
.
[2]
Jeffrey K. Bassett,et al.
An Analysis of Cooperative Coevolutionary Algorithms A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University
,
2003
.
[3]
J. Pollack,et al.
A game-theoretic investigation of selection methods used in evolutionary algorithms
,
2000,
Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).
[4]
Rudolf Paul Wiegand,et al.
An analysis of cooperative coevolutionary algorithms
,
2004
.