Showing papers on "Evolutionary programming published in 1996"

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms

Abstract: Introduction PART I: A COMPARISON OF EVOLUTIONARY ALGORITHMS 1. Organic Evolution and Problem Solving 2. Specific Evolutionary Algorithms 3. Artificial Landscapes 4. An Empirical Comparison PART II: EXTENDING GENETIC ALGORITHMS 5. Selection 6. Mutation 7. An Experiment in Meta-Evolution Summary and Outlook Appendix A: Data for the Fletcher-Powell Function Appendix B: Data from Selection Experiments Appendix D: The Multiprocessor Environment Appendix E: Mathematical Symbols Bibliography Index

Evolutionary algorithms for constrained parameter optimization problems

Abstract: Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.

Perspective: complex adaptations and the evolution of evolvability

Abstract: The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian process of mutation, recombination and selection is not universally effective in improving complex systems like computer programs or chip designs. For adaptation to occur, these systems must possess "evolvability," i.e., the ability of random variations to sometimes produce improvement. It was found that evolvability critically depends on the way genetic variation maps onto phenotypic variation, an issue known as the representation problem. The genotype-phenotype map determines the variability of characters, which is the propensity to vary. Variability needs to be distinguished from variations, which are the actually realized differences between individuals. The genotype-phenotype map is the common theme underlying such varied biological phenomena as genetic canalization, developmental constraints, biological versatility, developmental dissociability, and morphological integration. For evolutionary biology the representation problem has important implications: how is it that extant species acquired a genotype-phenotype map which allows improvement by mutation and selection? Is the genotype-phenotype map able to change in evolution? What are the selective forces, if any, that shape the genotype-phenotype map? We propose that the genotype-phenotype map can evolve by two main routes: epistatic mutations, or the creation of new genes. A common result for organismic design is modularity. By modularity we mean a genotype-phenotype map in which there are few pleiotropic effects among characters serving different functions, with pleiotropic effects falling mainly among characters that are part of a single functional complex. Such a design is expected to improve evolvability by limiting the interference between the adaptation of different functions. Several population genetic models are reviewed that are intended to explain the evolutionary origin of a modular design. While our current knowledge is insufficient to assess the plausibility of these models, they form the beginning of a framework for understanding the evolution of the genotype-phenotype map.

Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions

Abstract: This paper develops an efficient, general economic dispatch (ED) algorithm for generating units with nonsmooth fuel cost functions. Based on the evolutionary programming (EP) technique, the new algorithm is capable of determining the global or near global optimal dispatch solutions in the cases where the classical Lagrangian based algorithms cease to be applicable. Effectiveness of the new algorithm is demonstrated on two example power systems and compared to that of the dynamic programming, simulated annealing, and genetic algorithms. Practical application of the developed algorithm is additionally verified on the Taiwan power (Taipower) system. Numerical results show that the proposed EP based ED algorithm can provide accurate dispatch solutions within reasonable time for any type of fuel cost functions.

Fast Evolutionary Programming.

Abstract: Evolutionary programming (EP) has been applied to many numerical and combinatorial optimisation problems successfully in recent years. One disadvantage of EP is its slow convergence to a good near optimum for some function optimisation problems. In this paper, we propose a fast EP (FEP) which uses a Cauchy instead of Gaussian mutation operator as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between the fast simulated annealing and the classical version. Extensive empirical studies have been carried out to evaluate the performance of FEP for diierent function optimisation problems. Fifty runs have been conducted for each of the 23 test functions in our studies. Our experimental results show that FEP performs much better than CEP for multi-modal functions with many local minima while being comparable to CEP in performance for unimodal and multi-modal functions with only a few local minima. We emphasise in the paper that no single algorithm can be the best for all problems. What we need is to identify the relationship between an algorithm and a class of problems which are most amenable to the algorithm.

Evolutionary computation: an overview

Abstract: We present an overview of the most important representatives of algorithms gleaned from natural evolution, so-called evolutionary algorithms. Evolution strategies, evolutionary programming, and genetic algorithms are summarized, with special emphasis on the principle of strategy parameter self-adaptation utilized by the first two algorithms to learn their own strategy parameters such as mutation variances and covariances. Some experimental results are presented which demonstrate the working principle and robustness of the self-adaptation methods used in evolution strategies and evolutionary programming. General principles of evolutionary algorithms are discussed, and we identify certain properties of natural evolution which might help to improve the problem solving capabilities of evolutionary algorithms even further.

Evolutionary Search and the Job Shop

Abstract: 1. Introduction.- 2. Job Shop Scheduling.- 3. Local Search Techniques.- 4. Evolutionary Algorithms.- 5. Perspectives on Adaptive Scheduling..- 6. Population Flow in Adaptive Scheduling.- 7. Adaptation of Structured Populations.- 8. A Computational Study.- 9. Conclusions and Outlook.- References.

Self adaptation of mutation rates in a steady state genetic algorithm

Abstract: This paper investigates the use of genetically encoded mutation rates within a "steady state" genetic algorithm in order to provide a self-adapting mutation mechanism for incremental evolution. One of the outcomes of this work will be a reduction in the number of parameters required to be set by the operator, thus facilitating the transfer of evolutionary computing techniques into an industrial setting. The NK family of landscapes is used to provide a variety of different problems with known statistical features in order to examine the effects of changing various parameters on the performance of the search. A number of policies are considered for the replacement of members of the population with newly created individuals and recombination of material between parents, and a number of methods of encoding for mutation rate are investigated. Empirical comparisons (using the "best-of current-population" metric) over a range of test problems show that a genetic algorithm incorporating the best "flavour" of the adaptive mutation operator outperformed the same algorithm when using any one of a variety of "standard" fixed mutation rates suggested by other authors.

Evolutionary Computation at the Edge of Feasibility

Abstract: Numerical optimization problems enjoy a significant popularity in evolutionary computation community; all major evolutionary techniques use such problems for various tests and experiments. However, many of these techniques (as well as other, classical optimization methods) encounter difficulties in solving some real-world problems which include non-trivial constraints. This paper discusses a new development which is based on the observation that very often the global solution lies on the boundary of the feasible region. Thus, for many constrained numerical optimization problems it might be beneficial to limit the search to that boundary, using problem-specific operators. Two test cases illustrate this approach: specific operators are designed from the simple analytical expression of the constraints. Some possible generalizations to larger classes of constraints are discussed as well.

Evolutionary algorithms in computer-aided molecular design

Abstract: In recent years, search and optimisation algorithms inspired by evolutionary processes have been applied with marked success to a wide variety of problems in diverse fields of study. In this review, we survey the growing application of these 'evolutionary algorithms' in one such area: computer-aided molecular design. In the course of the review, we seek to summarise the work to date and to indicate where evolutionary algorithms have met with success and where they have not fared so well. In addition to this, we also attempt to discern some future trends in both the basic research concerning these algorithms and their application to the elucidation, design and modelling of chemical and biochemical structures.

Scheduling short-term hydrothermal generation using evolutionary programming techniques

Abstract: The authors presents a novel evolutionary programming (EP) based algorithm for the short-term hydrothermal scheduling problem. To more realistically represent the relationship between the generation and amount of water discharge for hydroaggregates, the generation models of the hydro plants as well as thermal plants are often expressed as nonlinear and nonsmooth curves with prohibited areas. The advantage of the proposed algorithm is that it is capable of determining the global or near global optimal solutions to such an optimisation problem with multiple local minima. The developed algorithm is illustrated and tested on two model systems. The test results are compared with those obtained using gradient search, simulated annealing and genetic algorithm methods. Numerical results show that the proposed EP-based algorithm can provide accurate solutions within a reasonable time.

Self-Adaptive Genetic Algorithm for Numeric Functions

Abstract: Self-adaption is one of the most promising areas of research in evolutionary computation as it adapts the algorithm to the problem while solving the problem. In this paper we extend self-adaption to operate on more than one aspect of evolutionary computation and at more than one level of adaption. We developed a genetic algorithm which selfadapts both mutation strength and population size; the results indicate that the approach works quite well.

Towards Evolvable Hardware: The Evolutionary Engineering Approach

Abstract: Field programmable gate array (FPGA) circuits.- Evolutionary algorithms.- Artificial cellular development in optimization and compilation.- CAM-Brain the evolutionary engineering of a billion neuron artificial brain by 2001 which grows/evolves at electronic speeds inside a cellular automata machine (CAM).- Morphogenesis for evolvable systems.- Evolvable Hardware and its application to pattern recognition and fault-tolerant systems.- Unconstrained evolution and hard consequences.- Embryonics: The birth of synthetic life.- Embryonics: A new family of coarse-grained field-programmable gate array with self-repair and self-reproducing properties.- Evolution and mobile autonomous robotics.- Development and evolution of hardware behaviors.

Learning Algorithms: Theory and Applications in Signal Processing

Abstract: From the Publisher: Over the past decade, interest in computational or non-symbolicartificial intelligence has grown. The algorithms involved have the ability to learn from past experience, and therefore have significant potential in the adaptive control of signals and systems. This book focuses on the theory and applications of learning algorithms-stochastic learning automata; artificial neural networks; and genetic algorithms, evolutionary strategies, and evolutionary programming. Hybrid combinations of various algorithms are also discussed. Chapter 1 provides a brief overview of the topics discussed and organization of the text. The first half of the book (Chapters 2 through 4) discusses the basic theory of the learning algorithms, with one chapter devoted to each type. In the second half (Chapters 5 through 7), the emphasis is on a wide range of applications drawn from adaptive signal processing, system identification, and adaptive control problems in telecommunication networks. Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications is an excellent text for final year undergraduate and first year graduate students in engineering, computer science, and related areas. Professional engineers and everyone involved in the application of learning techniques in adaptive signal processing, control, and communications will find this text a valuable synthesis of theory and practical application of the most useful algorithms.

A Testbed for Solving Optimization Problems Using Cultural Algorithms.

Abstract: This paper introduce a cultural algorithm based testbed which allows one to plug and play various combinations of evolution components for solving constrained numerical optimization. Our cultural algorithm framework combines weak search method with knowledge representation scheme for collecting and reasoning knowledge about individual experience. Currently genetic algorithm based software package GENOCOP(GEnetic algorithm for Numerical Optimization for COnstrained Problems) and rudimentary EP(Evolutionary Programming) are embedded in the cultural algorithm framework. Preliminary results suggest that the belief space is an important contributor to the problem solving process for both systems when the number of constraints on the problem become large enough.

Evolutionary programming approach to reactive power planning

Abstract: The paper proposes an application of evolutionary programming (EP) to reactive power planning (RPP). RPP is a nonsmooth and nondifferentiable optimisation problem for a multiobjective function. Several techniques to make EP practicable have been developed. The proposed approach is demonstrated with the IEEE 30-bus system. The comprehensive simulation results show that EP is a suitable method to solve the RPP problem. A conventional optimisation method is used as the comparison method. The comparison shows that EP is better than the conventional method in the RPP problem.

Evolving continuous behaviors in the Iterated Prisoner's Dilemma

Abstract: Evolutionary programming experiments are conducted on a variant of the Iterated Prisoner's Dilemma. Rather than assume each player having two alternative moves in the stage-game, cooperate or defect, a continuum of possible moves are available. Players' strategies are represented by feed-forward perceptrons with a single hidden layer. The population size and the number of nodes in the hidden layer are varied across a series of experiments. The results of the simulations indicate a minimum amount of complexity is required in a player's strategy in order for cooperation to evolve. Moreover, under the evolutionary dynamics of the simulation, cooperation does not appear to be a stable outcome.

Evolutionary design of artificial neural networks with different nodes

Abstract: Evolutionary design of artificial neural networks (ANNs) offers a very promising and automatic alternative to designing ANNs manually. The advantage of evolutionary design over the manual design is their adaptability to a dynamic environment. Most research in evolving ANNs only deals with the topological structure of ANNs and little has been done on the evolution of both topological structures and node transfer functions. The paper presents a new automatic method to design general neural networks (GNNs) with different nodes. GNNs combine generalisation capabilities of distributed neural networks (DNNs) and computational efficiency of local neural networks (LNNs). We use an evolutionary programming (EP) algorithm with new mutation operators which are very effective for evolving GNN architectures and weights simultaneously. Our EP algorithm allows GNNs to grow as well as shrink during the evolutionary process. Our experiment results show the effectiveness and accuracy of evolved GNNs.

Heuristic methods for evolutionary computation techniques

Abstract: Evolutionary computation techniques, which are based on a powerful principle of evolution—survival of the fittest, constitute an interesting category of heuristic search. In other words, evolutionary techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strife for survival. Any evolutionary algorithm applied to a particular problem must address the issue of genetic representation of solutions to the problem and genetic operators that would alter the genetic composition of offspring during the reproduction process. However, additional heuristics should be incorporated in the algorithm as well; some of these heuristic rules provide guidelines for evaluating (feasible and infeasible) individuals in the population. This paper surveys such heuristics and discusses their merits and drawbacks.

Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming

Abstract: From the Publisher: February 29-March 3, 1996, San Diego, California Evolutionary programming, originally conceived by Lawrence J. Fogel in 1960, is a stochastic and optimization method similar to genetic algorithms, but instead emphasizes the behavioral linkage between parents and their offspring, rather than emulating specific genetic operators as observed in nature. Evolutionary Programming V will serve as a reference and forum for researchers investigating applications and theory of evolutionary programming and other related areas in evolutionary and natural computation. Chapters describe original, unpublished research in evolutionary programming, evolution strategies, genetic algorithms and genetic programming, artificial life, cultural algorithms, and other dynamic models that rely on evolutionary principles. Topics include the use of evolutionary simulations in optimization, neural network training and design, automatic control, image processing and other applications, as well as mathematical theory or empirical analysis providing insight into the behavior of such algorithms. Of particular interest are applications of simulated evolution to problems in biology and economics. A Bradfor Book. Complex Adaptive Systems series

Shape Representations and Evolution Schemes.

Abstract: The choice of a representation i.e. the deeni-tion of the search space, is of vital importance in all Evolutionary Optimization processes. In the context of Topological Optimum Design in Structural Mechanics, this paper investigates possible representations for evolutionary shape design. The goal is the identiication of a shape in IR n (n = 2 or n = 3) having optimal mechanical properties. Evolutionary Computation has been demonstrated a valuable tool for TOD problems. However, all past results are based on the straightforward bitstring representation whose complexity increases with that of the underlying Mechanical model. To overcome this diiculty, diier-ent representations for shapes are introduced, and compared on the benchmark problem of TOD using various evolutionary schemes. The results are discussed with respect to the diierent degrees of epistasis of the representations.

Survey on Genetic Algorithm

Abstract: Genetic algorithm arised from evolutionary theory and genetics,which has become the important part of computation intelligence drawing much attention from many subjects.This paper offers an overview of the history,theory and applications for genetic algorithm. Relevant analysis and evaluation are provided.

Adaptively Parameterised Evolutionary Systems: Self-Adaptive Recombination and Mutation in a Genetic Algorithm

Abstract: It has long been recognised that the choice of recombination and mutation operators and the rates at which they are applied to a Genetic Algorithm will have a significant effect on the outcome of the evolutionary search, with sub-optimal values often leading to poor performance. In this paper an evolutionary algorithm (APES) is presented within which both the units of heredity and the probability that those units will subject to mutation are learnt via self adaptation of the genetic material. Using Kaufmann's NK model, this algorithm is compared to a number of combinations of frequently used crossover operators with “standard” mutation rates. The results demonstrate competitive times to find maxima on simple problems, and (on the most complex problems) results which are significantly better than the majority of other algorithms tested. This algorithm represents a robust adaptive search method which is not dependant on expert knowledge of genetic algorithm theory or practice in order to perform effectively.

Identification and control of systems with friction using accelerated evolutionary programming

Abstract: This article proposes a novel evolutionary algorithm, called accelerated evolutionary programming (AEP), which improves evolutionary programming in terms of convergence speed and diversity. Comparison between the proposed algorithm and evolutionary programming is carried out for five widely used test functions to show the effectiveness of the proposed algorithm. The proposed algorithm is applied to the identification of a seven-parameter friction model of an X-Y table, which is adopted from the results of tribology studies. Based on the identified friction model, a compensator is designed for the control of the X-Y table without stick-slip motion at very low velocity. Experimental results on the X-Y table demonstrate the effectiveness of the proposed scheme, especially for very-low-speed tracking.

The Evolutionary Development Model for Software

Abstract: The traditional waterfall life cycle has been the mainstay for software developers for many years. For software products that do not change very much once they are specified, the waterfall model is still viable. However, for software products that have their feature sets redefined during development because of user feedback and other factors, the traditional waterfall model is no longer appropriate.

Learning Genetic Representations as Alternative to Hand-Coded Shape Grammars

Abstract: Shape grammars have been used to analyze and describe designs, and to create new designs that are similar in style to the designs the grammar is based on. The grammars are created by hand, involving a large amount of research about the designs and the design process. This paper proposes a different approach, where a system is given design examples, and in a bottom-up process learns stylistic features of the examples. This is achieved by using an evolutionary system that is able to change the representation it is using. With the creation of a more and more complex evolved representation, the search space of the evolutionary process is transformed so that the search for new designs is biased towards designs similar to the design examples.

Evolving wavelet neural networks for function approximation

Abstract: A wavelet-based neural network with evolutionary programming is proposed. Unlike conventional backpropagation training algorithms, the evolutionary programming does not require gradient information and can provide a stochastic optimal search. Evolutionary programming is applied to optimise the wavelet neural network for function approximation. Experimental results are presented to show the potential of the evolutionary wavelet neural networks.