More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …

The success of evolutionary search depends on adequate parameter settings. Ill conditioned
strategy parameters decrease the success probabilities of genetic operators. Proper settings …

This is the second edition of our 2003 book. It is primarily a book for lecturers and graduate
and undergraduate students. To this group the book offers a thorough introduction to …

Genetic algorithms (GAs) have become popular as a means of solving hard combinatorial
optimization problems. The first part of this chapter briefly traces their history, explains the …

Although particle swarm optimization algorithm (PSO) has displayed promising performance
on many optimization problems, how to balance contradictions between the exploration and …

This paper presents ACROMUSE, a novel genetic algorithm (GA) which adapts crossover,
mutation, and selection parameters. ACROMUSEs objective is to create and maintain a …

As an intelligent search optimization technique, genetic algorithm (GA) is an important
approach for non-deterministic polynomial (NP-hard) and complex nature optimization …

Incremental social learning (ISL) was proposed as a way to improve the scalability of
systems composed of multiple learning agents. In this paper, we show that ISL can be very …

Parameter control is critical to the performance of any evolutionary algorithm (EA). In this
paper, we propose a Q-Learning-based Particle Swarm Optimization (QLPSO) algorithm …

Satellite imaging mission planning is used to optimize satellites to obtain target images
efficiently. Many evolutionary algorithms (EAs) have been proposed for satellite mission …