Evolutionary Computing

Formal memetic algorithms.- A statistical mechanical formulation of the dynamics of genetic algorithms.- Evolutionary stability in simple classifier systems.- Nonbinary transforms for genetic algorithm problems.- Enhancing evolutionary computation using analogues of biological mechanisms.- Exploiting mate choice in evolutionary computation: Sexual selection as a process of search, optimization, and diversification.- An empirical comparison of selection methods in evolutionary algorithms.- An evolution strategy and genetic algorithm hybrid: An initial implementation and first results.- Genetic algorithms and directed adaptation.- Genetic algorithms and neighbourhood search.- A unified paradigm for parallel Genetic Algorithms.- Distributed coevolutionary genetic algorithms for multi-criteria and multi-constraint optimisation.- Inductive operators and rule repair in a hybrid genetic learning system: Some initial results.- Adaptive learning of a robot arm.- Co-evolving Co-operative populations of rules in learning control systems.- Learning anticipatory behaviour using a delayed action classifier system.- Applying a restricted mating policy to determine state space niches using immediate and delayed reinforcement.- A comparison between two architectures for searching and learning in maze problems.- Fast practical evolutionary timetabling.- Optimising a presentation timetable using evolutionary algorithms.- Genetic algorithms and flowshop scheduling: towards the development of a real-time process control system.- Genetic algorithms for digital signal processing.- Complexity reduction using expansive coding.- The application of genetic programming to the investigation of short, noisy, chaotic data series.