By Ruhul Sarker, Masoud Mohammadian, Xin Yao (auth.)

Evolutionary computation thoughts have attracted expanding att- tions lately for fixing advanced optimization difficulties. they're extra strong than conventional tools in accordance with formal logics or mathematical programming for lots of actual global OR/MS difficulties. E- lutionary computation suggestions can care for complicated optimization difficulties higher than conventional optimization innovations. even if, so much papers at the program of evolutionary computation concepts to Operations learn /Management technological know-how (OR/MS) difficulties have scattered round in several journals and convention complaints. additionally they are likely to specialise in a truly designated and slim subject. it's the correct time that an archival ebook sequence publishes a different quantity which - cludes serious studies of the state-of-art of these evolutionary com- tation innovations that have been chanced on relatively necessary for OR/MS difficulties, and a set of papers which characterize the newest devel- ment in tackling a number of OR/MS difficulties via evolutionary computation innovations. This designated quantity of the ebook sequence on Evolutionary - timization goals at filling during this hole within the present literature. The specific quantity comprises invited papers written by means of best - searchers within the box. All papers have been peer reviewed by way of at the least recognized reviewers. The publication covers the basis in addition to the sensible part of evolutionary optimization.

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It can also be generated at random. can be generated similarly. , the standard deviation). 2 Evaluate the fitness value for each individual of the population. 3 Each parent so that a total of and creates offspring on average, offspring are generated: for where and denote the component of the vectors , and , respectively. N(0,1) denotes a normally distributed one-dimensional random number with mean zero and standard deviation one. indicates that the random number is generated anew for each value of The factors and are usually set to Schwefel, 1993).

Connectivities of ANNs (Yao and Shi, 1995; Liu and Yao, 1996; Yao and Liu, 1997a; Yao and Liu, 1998). Very good results have been achieved for some artificial and real-world benchmark problems. Evolutionary Computation 39 One of the most important benefits of evolutionary artificial neural networks is that a near optimal (in terms of generalisation) artificial neural network with both structure and weights can be evolve automatically without going through a tedious trial-and-error manual design process.

Some of the evolutionary algorithms can hardly be classified into any of these three categories. Evolutionary computation includes much more than just three kinds of algorithms. It also covers topics such as artificial immune systems, artificial ecological systems, co-evolutionary systems, evolvable hardware, self-adaptive systems, etc. 2. , evolutionary computation theory, evolutionary optimisation and evolutionary learning. There are, of course, overlaps among these areas. 1 Evolutionary Computation Theory The theoretical work in evolutionary computation has concentrated on three main topics.

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