On the Evolution of Evolutionary Algorithms

2007

A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several wellknown benchmarking problems.

The field now called Evolutionary Computation had a slow start. In the late 60s and early 70s a number of researchers in the USA and Germany applied the principles of Darwinian evolution, based on natural selection, for problem solving. Independently from each other they established the power of evolutionary techniques and worked on the theory and applications of their own approach. These were the times of rather separate development of Genetic Algorithms, Evolution Strategies and Evolutionary Programming. From the early 90s it is more and more acknowledged that the different approaches share the same basic principles, while differing only in technical details, terminology and sometimes in the philosophy behind it. In the meanwhile, a new branch, called Genetic Programming, has also emerged and joined the family. The entire family of algorithms is called nowadays the family of Evolutionary Algorithms-a name attempting to cover all the aforementioned techniques, and even more. An Evolutionary Algorithm (EA) can actually be any population-based, stochastic search algorithm that uses a (heuristic) quality measure, called fitness, of candidate solutions and applies reproduction operators to create, and fitness-based selection to reduce, diversity in the population.

Genetic Programming and Evolvable Machines, 2007

Proceedings of the Fifth International Conference …, 1993

Evolutionary algorithm (EA) is a generic population-based metaheuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the tness function determines the environment within which the solutions "live" . Evolution of the population then takes place after the repeated application of the above operators. This description is deliberately based on a unifying view resenting a general scheme that forms the common basis of all volutionary Algorithm variants. The main components of EAs are discussed, explaining their role and related issues of terminology. Further on we discuss general issues for EAs concerning their working. Finally, we put EAs into a broader context and explain their relation with other global optimisation techniques.

European Journal of Operational Research, 2000

Evolutionary algorithms (EA) are optimisation techniques inspired from natural evolution processes. They handle a population of individuals that evolve with the help of information exchange procedures. Each individual may also evolve independently. Periods of co-operation alternate with periods of self-adaptation. We de®ne a terminology and give a general framework for the description of the main features of any particular evolutionary algorithm. Such a description does not provide, nor does it replace, algorithm pseudo-codes. The aim is to develop tools that may help understanding the``philosophy'' of such methods. Ó

Journal of Artificial Evolution and Applications, 2010

Biological and artificial evolutionary systems exhibit varying degrees of evolvability and different rates of evolution. Such quantities can be affected by various factors. Here, we review some evolutionary mechanisms and discuss new developments in biology that can potentially improve evolvability or accelerate evolution in artificial systems. Biological notions are discussed to the degree they correspond to notions in Evolutionary Computation. We hope that the findings put forward here can be used to design computational models of evolution that produce significant gains in evolvability and evolutionary speed.

Soft Computing, 2007

A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the Multi Expression Programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme which is used for solving a particular problem. Several evolutionary algorithms for function optimization are evolved by using the considered model. The evolved evolutionary algorithms are compared with a human-designed Genetic Algorithm. Numerical experiments show that the evolved evolutionary algorithms can compete with standard approaches for several well-known benchmarking problems.

This article provides a brief overview of the field of Evolutionary Computation. It describes the important historical developments that shaped the field. It summarizes the field as it exists today and discusses some of the important directions in which the field is developing. Copyright © 2009 John Wiley & Sons, Inc.For further resources related to this article, please visit the WIREs website.

Machine Learning: ECML …, 1993