Though recent analysis of traditional cooperative coevolutionary algorithms (CCEAs) casts doubt on their suitability for static optimization tasks, our experience is that the algorithms perform quite well in multiagent learning settings. This is due in part because many CCEAs may be quite suitable to finding behaviors for team members that result in good (though not necessarily optimal) performance but which are also robust to changes in other team members. Given this, there are two main goals of this paper. First, we describe a general framework for clearly defining robustness, offering a specific definition for our studies. Second, we examine the hypothesis that CCEAs exploit this robustness property during their search. We use an existing theoretical model to gain intuition about the kind of problem properties that attract populations in the system, then provide a simple empirical study justifying this intuition in a practical setting. The results are the first steps toward a constructive view of CCEAs as optimizers of robustness.

Seeding the population of an evolutionary algorithm with solutions from previous runs has proved to be useful when learning control strategies for agents operating in a complex, changing environment. It has generally been assumed that initializing a learning algorithm with previously learned solutions will be helpful if the new problem is similar to the old. We will show that this assumption sometimes does not hold for many reasonable similarity metrics. Using a more traditional machine learning perspective, we explain why seeding is sometimes not helpful by looking at the learning-experience bias produced by the previously evolved solutions.

Several researchers have used Price's equation (from biology theory literature) to analyze the various components of an Evolutionary Algorithm (EA) while it is running, giving insights into the components contributions and interactions. While their results are interesting, they are also limited by the fact that Price's equation was designed to work with the averages of population fitness. The EA practitioner, on the other hand, is typically interested in the best individuals in the population, not the average.In this paper we introduce an approach to using Price's equation which instead calculates the upper tails of population distributions. By applying Price's equation to EAs that use survival selection instead of parent selection, this information is calculated automatically.

The term "premature convergence" has been used for many years as an explanation as to why an evolutionary algorithm fails to find a global optimum, without providing much insight into how to fix the problem and/or avoid it in the future. In this paper we tie these issues to notions of (lack of) evolvability that have been explored in the population genetics community for many years. In particular, we show how the central equation in Price's theorem can be extended in such a way as to separate out the individual contributions that reproductive operators make to evolvability, paving the way for better designed EAs in the future.

To successfully apply evolutionary algorithms to the solution of increasingly complex problems, we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. One of the major difficulties is finding computational extensions to our current evolutionary paradigms that will enable such subcomponents to "emerge" rather than being hand designed. In this paper, we describe an architecture for evolving such subcomponents as a collection of cooperating species. Given a simple string-matching task, we show that evolutionary pressure to increase the overall fitness of the ecosystem can provide the needed stimulus for the emergence of an appropriate number of interdependent subcomponents that cover multiple niches, evolve to an appropriate level of generality, and adapt as the number and roles of their fellow subcomponents change over time. We then explore these issues within the context of a more complicated domain through a case study involving the evolution of artificial neural networks.