Showing papers on "Evolutionary computation published in 1993"

An overview of evolutionary algorithms for parameter optimization

Abstract: Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs), evolutionary programming (EP), and genetic algorithms (GAs). The comparison is performed with respect to certain characteristic components of EAs: the representation scheme of object variables, mutation, recombination, and the selection operator. Furthermore, each algorithm is formulated in a high-level notation as an instance of the general, unifying basic algorithm, and the fundamental theoretical results on the algorithms are presented. Finally, after presenting experimental results for three test functions representing a unimodal and a multimodal case as well as a step function with discontinuities, similarities and differences of the algorithms are elaborated, and some hints to open research questions are sketched.

Genetic algorithms: principles of natural selection applied to computation

Abstract: A genetic algorithm is a form of evolution that occurs on a computer. Genetic algorithms are a search method that can be used for both solving problems and modeling evolutionary systems. With various mapping techniques and an appropriate measure of fitness, a genetic algorithm can be tailored to evolve a solution for many types of problems, including optimization of a function of determination of the proper order of a sequence. Mathematical analysis has begun to explain how genetic algorithms work and how best to use them. Recently, genetic algorithms have been used to model several natural evolutionary systems, including immune systems.

Evolution and Optimum Seeking: The Sixth Generation

Abstract: From the Publisher: With the publication of this book, Hans-Paul Schwefel has responded to rapidly growing interest in Evolutionary Computation, a field that originated, in part, with his pioneering work in the early 1970s. Evolution and Optimum Seeking offers a systematic overview of both new and classical approaches to computer-aided optimum system design methods, including the new class of Evolutionary Algorithms and other "Parallel Problem Solving from Nature" (PPSN) methods. It presents numerical optimization methods and algorithms to computer calculations which will be particularly useful for massively parallel computers. It is the only book in the field that offers in-depth comparisons between classical direct optimization methods and the newer methods. Dr. Schwefel's method consists essentially of the adaptation of simple evolutionary rules to a computer procedure in the search for optimal parameters within a simulation model of a technical device. In addition to its historical and practical value, Evolution and Optimum Seeking will stimulate further research into PPSN and interdisciplinary thinking about multi-agent self-organization in natural and artificial environments. These developments have been accelerated by fortunate changes in the computational environment, especially with respect to new architectures. MIMD (Multiple Instructions Multiple Data) machines with many processors working in parallel on one task seem to lend themselves to inherently parallel problem solving concepts like Evolution Strategies. The most comprehensive work of its kind, Evolution and Optimum Seeking offers a state-of-the-art perspective on the field for researchers in computer-aided design, planning, control, systems analysis, computational intelligence, and artificial life. Its range and depth make it a virtual handbook for practitioners: epistemological introduction to the concepts and strategies of optimum seeking; taxonomy of optimization tasks and solution principles (material found n

An overview of evolutionary computation

Abstract: Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computerbased problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest. Important similarities and differences are noted, which lead to a discussion of important issues that need to be resolved, and items for future research.

Applying evolutionary programming to selected traveling salesman problems

Abstract: Natural evolution provides a paradigm for the design of stochastic-search optimization algorithms. Various forms of simulated evolution, such as genetic algorithms and evolutionary programming techniques, have been used to generate machine learning through automated discovery. These methods have been applied to complex combinatorial optimization problems with varied degrees of success. The present paper relates the use of evolutionary programming on selected traveling salesman problems. In three test cases, solutions that are equal to or better than previously known best routings were discovered. In a 1000-city problem, the best evolved routing is about 5% longer than the expected optimum.

Spacetime constraints revisited

Abstract: The Spacetime Constraints (SC) paradigm, whereby the animator specifies what an animated figure should do but not how to do it, is a very appealing approach to animation. However, the algorithms available for realizing the SC approach are limited. Current techniques are local in nature: they all use some kind of perturbational analysis to refine an initial trajectory. We propose a global search algorithm that is capable of generating multiple novel trajectories for SC problems from scratch. The key elements of our search strategy are a method for encoding trajectories as behaviors, and a genetic search algorithm for choosing behavior parameters that is currently implemented on a massively parallel computer. We describe the algorithm and show computed solutions to SC problems for 2D articulated figures. CR Categories: I.2.6 [Artificial Intelligence]: Learning— parameter learning. I.2.6 [Artificial Intelligence]: Problem Solving, Control Methods and Search—heuristic methods. I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism— animation. I.6.3 [Simulation and Modeling]: Applications. Additional

Evolutionary Module Acquisition

Abstract: Evolutionary programming and genetic algorithms share many features, not the least of which is a reliance of an analogy to natural selection over a population as a means of implementing search. With their commonalities come shared problems whose solutions can be investigated at a higher level and applied to both. One such problem is the manipulation of solution parameters whose values encode a desirable sub-solution. In this paper, we define a superset of evolutionary programming and genetic algorithms, called evolutionary algorithms, and demonstrate a method of automatic modularization that protects promising partial solutions and speeds acquisition time.

New evolutionary genetic algorithms for NP-complete combinatorial optimization problems

Abstract: Evolutionary genetic algorithms have been proposed to solve NP-complete combinatorial optimization problems. A new crossover operator based on group theory has been created. Computational processes motivated by proposed evolutionary genetic algorithms were described as stochastic processes, using population dynamics and interactive markovian chains. The proposed algorithms were used in solving flowshop problems and an asymmetric traveling salesman problem. The experimental results showed the superiority of new evolutionary algorithms in comparison with the standard genetic algorithm.

Structure evolution and incomplete induction

Abstract: Evolutionary strategies such as the evolution strategy (Rechenberg 1965, 1973; Schwefel 1977) or genetic algorithms (Holland 1975; Goldberg 1989) have been widely applied to systems where parameters have to be determined according to a particular objective function A necessary demand in all these experiments is that the structures of the objects to be optimised are well defined, because these structures are part of the objective function With structure evolution the range of applications of evolutionary algorithms can now be expanded to tasks which are less accurately described, ie where the structures of the objects are fairly unknown Heuristical effort is reduced first to defining structure components by combinations of which the structure space is generated The structure space can be nearly infinitely large Furthermore, the mutation procedures for structures have to be determined, complying with the demand for strong causality In its computer model the algorithm of structure evolution involves the phenomenon of isolation, a feature of biological evolution additional to replication, mutation, and selection, which have already been implemented in other strategies The idea of structure evolution is to let different but some what similar structures of an object compete in temporarily isolated populations where the respective parameter evolution is carried out Thus structure evolution can perform a most effective search, both in structure and parameter space The algorithm is demonstrated with two examples: a neural filter in a visual system and the topologies of frameworks The first of the examples touches the problem of incompletely described tasks, and this paper will show that the effect of "overlearning" can be avoided by a learning procedure called "incomplete induction", which fits best with the algorithm of structure evolution

A Perspective on Evolutionary Computation

Abstract: During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The emergence of massively parallel computers made these algorithms of practical interest. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution strategies, simulated annealing, classifier systems, and neural networks.

Multivalued Evolutionary Algorithms

Abstract: One feature of evolving populations is that genetic operators act on the genotypic level while selection acts on the phenotypic level. We call the transformation between genes and phenes a developmental process because of its dynamics. Generally, no inferences can be made from phenes to genes, i.e. the mapping from genes to phenes is not an isomorphic one as it is assumed in Genetic Algorithms and Evolution Strategies. Based on this observation we present a Multival-ued Evolutionary Algorithm (MEA).

Testing parallel evolution strategies on the quadratic assignment problem

Abstract: Parallel evolution strategies are demonstrating to be worthwhile in a variety of contexts. In this paper, besides the classical genetic and evolutionary strategies, a hybrid evolutionary approach which incorporates memory of the search history within the structure is analyzed. The parallel evolution algorithms are mapped on a distributed memory MIMD multicomputer whose processors are configured in a torus topology. The simulations are conducted using the quadratic assignment problem as an artificial environment. The relationship between genetic representations and recombination operators is investigated. The experimental results obtained show the value of structures richer than bit strings and the effectiveness of memory for the evolution process. >

New Evolutionary Genetic Algorithms for Combinatorial Optimization Problems

Abstract: This paper deals with the NP-complete combinatorial optimization problems. A large number of papers has been devoted to them. A traditional approach of Operational Research has been used (Lawler et al. 1985). Kirkpatrick et al. (1983) used Simulated Annealing. Hopfield and Tank (1985) applied a neural networks for finding suboptimal solution for TSP. In recent years interest has raised to apply evolutionary algorithms to combinatorial optimization problems (Holland 1975; Brady 1985).

The Calculus of Self-Modifiable Algorithm Based Evolutionary Computer Network Routing

Abstract: The Calculus of Self-Modifiable Algorithms (CSA) is a universal approach to parallel and intelligent system. Its aim is to integrate different styles of programming and is applied to different areas of future generation computers. Potential applications of CSA include expert systems, machine learning, adaptive systems and many others. The problem of route optimization in computer networks is identified as a task that requires some sort of cost-driven solution that allows for the computation of paths in a network based on experience and inference. The Calculus of Self-Modifiable Algorithm is used to do the specification of this problem by modeling a system of machine learning algorithms that learn proper routing techniques for a particular computer network by incorporating an apportionment of credit system and various rule discovery concepts similar to the learning techniques used in evolutionary computing and symbolic learning.