Cyclic Incrementality in Competitive Coevolution: Evolvability through Pseudo-Baldwinian Switching-Genes

Coevolving systems are notoriously difficult to understand. This is largely due to the Red Queen effect that dictates heterospecific fitness interdependence. In simulation studies of coevolving systems, master tournaments are often used to obtain more informed fitness measures by testing evolved individuals against past and future opponents. However, such tournaments still contain certain ambiguities. We introduce the use of a phenotypic cluster analysis to examine the distribution of opponent categories throughout an evolutionary sequence. This analysis, adopted from widespread usage in the bioinformatics community, can be applied to master tournament data. This allows us to construct behavior-based category trees, obtaining a hierarchical classification of phenotypes that are suspected to interleave during cyclic evolution. We use the cluster data to establish the existence of switching-genes that control opponent specialization, suggesting the retention of dormant genetic adaptations, that is, genetic memory. Our overarching goal is to reiterate how computer simulations may have importance to the broader understanding of evolutionary dynamics in general. We emphasize a further shift from a component-driven to an interaction-driven perspective in understanding coevolving systems. As yet, it is unclear how the sudden development of switching-genes relates to the gradual emergence of genetic adaptability. Likely, context genes gradually provide the appropriate genetic environment wherein the switching-gene effect can be exploite

Evolutionary Computation Applied to Adaptive Information Filtering

Information Filtering is concerned with filtering data streams in such a way as to leave only pertinent data (information) to be perused. When the data streams are produced in a changing environment the filtering has to adapt too in order to remain effective. Adaptive Information Filtering is concerned with filtering in changing environments. The changes may occur both on the transmission side (the nature of the streams can change), and on the reception side (the interest of a user can change). Weighted trigram analysis is a quick and flexible technique for describing the contents of a document. A novel application of evolutionary computation is its use in adaptive information filtering for optimizing various parameters, notably the weights associated with trigrams. The research described in this paper combines weighted trigram analysis, clustering, and a special two-pool evolutionary algorithm, to create an Adaptive Information Filtering system with such use ful properties as domain independence, spelling error insensitivity, adaptability, and optimal use of user feedback while minimizing the amount of user feedback required to function properly. We designed a special evolutionary algorithm with a two-pool strategy for this changing environment

Adaptive Information Filtering: Evolutionary Computation and N-gram Representation

Abstract Adaptive Information Filtering (AIF) is concerned with filtering information streams in changing environments. The changes may occur both on the transmission side (the nature of the streams can change) and on the reception side (the interests of a user can change). The research described in this paper details the progress made in a prototype AIF system based on weighted ngram analysis and evolutionary computation. A major advance is the design and implementation of an n-gram class library allowing experimentation with different values of n instead of solely with 3-grams as in the past. The new prototype system was tested on the Reuters-21578 text categorization test collection