Lawrence Davis

TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.

TL;DR: A set of experiments performed on data from a sonar image classification problem are described to illustrate the improvements gained by using a genetic algorithm rather than backpropagation and chronicle the evolution of the performance of the genetic algorithm as it added more and more domain-specific knowledge into it.

TL;DR: This work extends the application of CPSO to the dynamic problem by considering a bi-modal parabolic environment of high spatial and temporal severity, and suggests that charged swarms perform best in the extreme cases, but neutral swarms are better optimizers in milder environments.

TL;DR: New techniques for applying adaptive algorithms to epistatic domains, while retaining some of the strength of Holland's convergence proof are described, for two-dimensional bin-packing problems and graph coloring problems.