Genetic and Evolutionary Computation

Genetic Algorithms and Evolutionary Computation

Evolutionary Computation and Genetic Algorithms

A genetic algorithm (GA) is a procedure used to find approximate solutions to search problems through the application of the principles of evolutionary biology. Genetic algorithms use biologically inspired techniques, such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover). Along with genetic programming (GP), they are one of the main clas...

Evolutionary Computation and Genetic Programming

We discuss Evolutionary Computation, in particular Genetic Programming, as examples of drawing inspiration from biological systems. We set the choice of evolution as a source for inspiration in context, discuss the history of Evolutionary Computation and its variants before looking more closely at Genetic Programming. After a discussion of methods and the state-of-theart, we review application ...

Introduction to Evolutionary Computation and Genetic Programming

Evolutionary computation is the study of non-deterministic search algorithms that are based on aspects of Darwin’s theory of evolution by natural selection [10]. The principal originators of evolutionary algorithms were John Holland, Ingo Rechenberg, Hans-Paul Schwefel and Lawrence Fogel. Holland proposed genetic algorithms and wrote about them in his 1975 book [19]. He emphasized the role of g...

Genetic and Evolutionary Computation: Medical Applications