Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP), by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.

In the work of communications network design there are several recurring themes: maximizing flows, finding circuits, and finding shortest paths or minimal cost spanning trees, among others. Some of these problems appear to be harder than others. For some, effective algorithms exist for solving them, for others, tight bounds are known, and for still others, researchers have few clues towards a good approach. One of these latter, nastier problems arises in the design of communications networks: the Optimal Communication Spanning Tree Problem (OCSTP). First posed by Hu in 1974, this problem has been shown to be in the family of NP-complete problems. So far, a good, general-purpose approximation algorithm for it has proven elusive. This thesis describes the design of a genetic algorithm for finding reliably good solutions to the OCSTP. The genetic algorithm approach was thought to be an appropriate choice since they are computationally simple, provide a powerful parallel search capability...

This paper presents some experimental results and analyses of the gene invariant genetic algorithm(GIGA). Although a subclass of the class of genetic algorithms, this algorithm and its variations represent a unique approach with many interesting results. The primary distinguishing feature is that when a pair of offspring are created and chosen as worthy of membership in the population they replace their parents. With no mutation this has the effect of maintaining the original genetic material over time, although it is reorganized. In this paper no mutation is allowed. The only genetic operator used is crossover. Several crossover operators are experimented with and analyzed. The notion of a family is introduced and different selection methods are analyzed. Tests using simple functions, the De Jong five function test suite and several deceptive functions are reported. GIGA performs as well as traditional GAs, and sometimes better. The evidence indicates that this method makes more effec...

This paper provides an introduction to genetic algorithms and genetic programming and lists sources of additional information, including books and conferences as well as e-mail lists and software that is available over the Internet. 1. GENETIC ALGORITHMS John Holland's pioneering book Adaptation in Natural and Artificial Systems (1975, 1992) showed how the evolutionary process can be applied to solve a wide variety of problems using a highly parallel technique that is now called the genetic algorithm. The genetic algorithm (GA) transforms a population (set) of individual objects, each with an associated fitness value, into a new generation of the population using the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring genetic operations such as crossover (sexual recombination) and mutation. Each individual in the population represents a possible solution to a given problem. The genetic algorithm attempts to find a very good (or best) ...

In an earlier paper we examined the relationship between genetic algorithms (GAs) and traditional methods of experimental design. This was motivated by an investigation into the problems caused by epistasis in the implementation and application of GAs to optimization problems. We showed how this viewpoint enables us to gain further insights into the determination of epistatic effects, and into the value of different forms of encoding a problem for a GA solution. We also demonstrated the equivalence of this approach toWalsh transform analysis. In this paper we consider further the question of whether the epistasis metric actually gives a good prediction of the ease or difficulty of solution of a given problem by a GA. Our original analysis assumed, as does the rest of the related literature, knowledge of the complete solution space. In practice, we only ever sample a fraction of all possible solutions, and this raises significant questions which are the subject of...

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This paper describes a new encoding scheme for the representation of spanning trees. This new encoding scheme is based on the factorization of the determinant of the in-degree matrix of the original graph. Each factor represents a spanning tree if the determinant corresponding to that factor is equal to one. Our new determinant encoding will be compared to the Prufer encoding, and to the node and link biased encoding by solving an NP-complete variation of the minimum spanning tree problem, known as the Probabilistic Minimum Spanning Tree Problem. Given a connected graph G(V,E), a cost function c:E;!< +, and a probability function P:2V;![0 � 1], the problem is to nd an a priori spanning tree of minimum expected length. Our results show a signi cant improvement in using the new determinant encoding and the node and link biased encoding compared to Prufer's encoding. We also show empirically that our new determinant encoding scheme is as good as the node and link biased encoding. Our new determinant encoding works very well for restricted spanning trees, and for incomplete graphs. 1

This paper describes the recently developed genetic programming paradigm which genetically breeds a population of computer programs to solve problems. The paper then shows, step by step, how to apply genetic programming to a problem of behavioral ecology in biology – specifically, two versions of the problem of finding an optimal food foraging strategy for the Caribbean Anolis lizard. A simulation of the adaptive behavior of the lizard is required to evaluate each possible adaptive control strategy considered for the lizard. The foraging strategy produced by genetic programming is close to the mathematical solution for the one version for which the solution is known and appears to be a reasonable approximation to the solution for the second version of the problem.

The Generalized Traveling Salesman Problem is a variation of the well known Traveling Salesman Problem in which the set of nodes is divided into clusters; the objective is to find a minimum-cost tour passing through one node from each cluster. We present an effective heuristic for this problem. The method combines a genetic algorithm (GA) with a local tour improvement heuristic. Solutions are encoded using random keys, which circumvent the feasibility problems encountered when using traditional GA encodings. On a set of 41 standard test problems with symmetric distances and up to 442 nodes, the heuristic found solutions that were optimal in most cases and were within 1% of optimality in all but the largest problems, with computation times generally within 10 seconds. The heuristic is competitive with other heuristics published to date in both solution quality and computation time.

We present a method for learning heuristics employed by an automated prover to control its inference machine. The hub of the method is the adaptation of the parameters of a heuristic. Adaptation is accomplished by a genetic algorithm. The necessary guidance during the learning process is provided by a proof problem and a proof of it found in the past. The objective of learning consists in finding a parameter configuration that avoids redundant effort w.r.t. this problem and the particular proof of it. A heuristic learned (adapted) this way can then be applied profitably when searching for a proof of a similar problem. So, our method can be used to train a proof heuristic for a class of similar problems. A number of experiments (with an automated prover for purely equational logic) show that adapted heuristics are not only able to speed up enormously the search for the proof learned during adaptation. They also reduce redundancies in the search for proofs of similar theorems. This not o...