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 …

Hybrid genetic algorithms have received significant interest in recent years and are being
increasingly used to solve real-world problems. A genetic algorithm is able to incorporate …

In this study, an improved crossover operator is suggested, for solving path planning
problems using genetic algorithms (GA) in static environment. GA has been widely applied …

This paper introduces many objective robust decision making (MORDM). MORDM combines
concepts and methods from many objective evolutionary optimization and robust decision …

Many real-world search and optimization problems involve inequality and/or equality
constraints and are thus posed as constrained optimization problems. In trying to solve …

The issue of controlling values of various parameters of an evolutionary algorithm is one of
the most important and promising areas of research in evolutionary computation: it has a …

Introduces the compact genetic algorithm (cGA) which represents the population as a
probability distribution over the set of solutions and is operationally equivalent to the order …

Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve
problems in many different disciplines. Parallel GAs are particularly easy to implement and …

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that
explore the space of potential solutions by building and sampling explicit probabilistic …

Chapter 4 GENETIC ALGORITHMS Page 1 Chapter 4 GENETIC ALGORITHMS Kumara Sastry,
David Goldberg University of Illinois, USA Graham Kendall University of Nottingham, UK 4.1 …