2000 WhatisEvolutionaryComputation

(Fogel, 2000) ⇒ David B. Fogel. (2000). “What is Evolutionary Computation?.” In: IEEE Spectrum, 37(2).

Subject Headings: Evolutionary Computation

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Abstract

Taking a page from Darwin's 'On the origin of the species', computer scientists have found ways to evolve solutions to complex problems. Harnessing the evolutionary process within a computer provides a means for addressing complex engineering problems-ones involving chaotic disturbances, randomness, and complex nonlinear dynamics-that traditional algorithms have been unable to conquer. Indeed, the field of evolutionary computation is one of the fastest growing areas of computer science and engineering for just this reason; it is addressing many problems that were previously beyond reach, such as rapid design of medicines, flexible solutions to supply-chain management problems, and rapid analysis of battlefield tactics for defense. Potentially, the field may fulfil the dream of artificial intelligence: a computer that can learn on its own and become an expert in any chosen area

1. Introduction

The principle of evolution is the primary unifying concept of biology, linking every organism together in a historical chain of events. Every creature in the chain is the product of a series of "accidents" that have been sorted out thoroughly under selective pressure from the environment. Over many generations, random variation and natural selection shape the behaviors of individuals and species to fit the demands of their surroundings.

This fit can be quite extraordinary and compelling [Fig. 1], a clear indication that evolution is creative. While evolution has no intrinsic purpose - it is merely the effect of physical laws acting on and within populations and species - it is capable of engineering solutions to the problems of survival that are unique to each individual's circumstance and, by any measure, quite ingenious.

Imagine what harnessing the evolutionary process within a computer might do. It could provide a means for addressing complex engineering problems - ones involving chaotic disturbances, randomness, and complex nonlinear dynamics-that our traditional algorithms have been unable to conquer. Indeed, the field of evolutionary computation is one of the fastest growing areas of computer science and engineering for just this reason; it is addressing many problems that were previously beyond reach, such as rapid design of medicines, flexible solutions to supply-chain management problems, and rapid analysis of battlefield tactics for defense. Potentially, the field may fulfill the dream of artificial intelligence: a computer that can learn on its own and become an expert in any chosen area.

In the most general terms, evolution can be described as a two-step iterative process, consisting of random variation followed by selection. The link between this description of evolution and the optimizing algorithms that are the hallmark of evolutionary computation is conceptually simple.

Just as natural evolution starts from an initial population of creatures, the algorithmic approach begins by selecting an initial set of contending solutions for a particular problem. The set may be chosen by generating solutions randomly or by utilizing any available knowledge about the problem.

These "parent" solutions then generate “offspring" by a preselected means of random variation. The resultant solutions are evaluated for their effectiveness-their "fitness' and undergo selection. Just as nature imposes the rule of "survival of the fittest," those solutions that are the least fit are removed from further consideration, and the process is repeated over successive generations [Fig. 2]

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2000 WhatisEvolutionaryComputation	David B. Fogel			What is Evolutionary Computation?