Showing papers on "Evolutionary programming published in 1999"

Evolutionary programming made faster

Abstract: Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In the paper, a "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. The paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. The paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near-optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.

Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications

Abstract: Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.

Parameter control in evolutionary algorithms

Abstract: The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: it has a potential of adjusting the algorithm to the problem while solving the problem. In the paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research.

Comparison of multiobjective evolutionary algorithms: empirical results

Abstract: In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.

A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques

Abstract: This paper presents a critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search capabilities of evolutionary algorithms Each technique is briefly described with its advantages and disadvantages, its degree of applicability and some of its known applications Finally, the future trends in this discipline and some of the open areas of research are also addressed

Rapid automated molecular replacement by ­evolutionary search

Abstract: A new procedure for molecular replacement is presented in which an efficient six-dimensional search is carried out using an evolutionary optimization algorithm. In this procedure, a population of initially random molecular-replacement solutions is iteratively optimized with respect to the correlation coefficient between observed and calculated structure factors. The sensitivity and reliability of the method is enhanced by uniform sampling of the rotational-search space and the use of continuously variable rotational and translational parameters. The process is several orders of magnitude faster than a systematic six-dimensional search, and comparisons show that it can identify solutions using significantly less accurate or less complete search models than is possible with two existing molecular-replacement methods. A program incorporating the method, EPMR, allows the rapid and highly automated solution of molecular-replacement problems involving single or multiple molecules in the asymmetric unit. EPMR has been used to solve a number of difficult molecular-replacement problems.

Particle swarm optimiser with neighbourhood operator

Abstract: In recent years population based methods such as genetic algorithms, evolutionary programming, evolution strategies and genetic programming have been increasingly employed to solve a variety of optimisation problems. Recently, another novel population based optimisation algorithm - namely the particle swarm optimisation (PSO) algorithm, was introduced by R. Eberhart and J. Kennedy (1995). Although the PSO algorithm possesses some attractive properties, its solution quality has been somewhat inferior to other evolutionary optimisation algorithms (P. Angeline, 1998). We propose a number of techniques to improve the standard PSO algorithm. Similar techniques have been employed in the context of self organising maps and neural-gas networks (T. Kohonen, 1990; T.M. Martinez et al., 1994).

Evolutionary Design by Computers

Abstract: Acknowledgements About the Editor Foreword List of Contributors 1 An Introduction to Evolutionary Design by Computers SECTION 1: Evolution and Design SECTION 2: Evolutionary Optimisation of Designs SECTION 3: Evolutionary Art SECTION 4: Evolutionary Artificial Life Forms SECTION 5: Creative Evolutionary Design Glossary Index

An evolutionary programming solution to the unit commitment problem

Abstract: The work was conducted with the aim of finding a general method for solving the unit commitment (UC) problem. The proposed algorithm employs the evolutionary programming (EP) technique in which populations of contending solutions are evolved through random changes, competition, and selection. In the subject algorithm an overall UC schedule is coded as a string of symbols and viewed as a candidate for reproduction. Initial populations of such candidates are randomly produced to form the basis of subsequent generations. The practical implementation of this procedure yielded satisfactory results when the EP-based algorithm was tested on a reported UC problem previously addressed by some existing techniques such as Lagrange relaxation (LR), dynamic programming (DP), and genetic algorithms (GAs). Numerical results for systems of up to 100 units are given and commented on.

Evolutionary programming based optimal power flow algorithm

Abstract: This paper develops an efficient and reliable evolutionary programming algorithm for solving the optimal power flow (OPF) problem. The class of curves used to describe generator performance does not limit the algorithm and the algorithm is also less sensitive to starting points. To improve the speed of convergence of the algorithm as well as its ability to handle larger systems, the algorithm is enhanced with gradient information. In the paper, the main elements of the evolutionary programming based OPF algorithm are presented. The algorithm is then demonstrated on the IEEE 30 bus test system.

Implementation of evolutionary fuzzy systems

Abstract: Evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionary algorithm are adapted via a fuzzy system. Benefits of the methodology are illustrated in the process of classifying the iris data set. Possible extensions of the methods are summarized.

Multiobjective evolutionary algorithm test suites

Abstract: Multiobjective Evolutionary Algorithms (MOEAs) currently have no generic benchmark test suites. This paper provides several Multiobjective Optimization Problems (MOPS) for use as part of a standardized MOEA test suite, and proposes a methodology whereby various MOEAs can be directly compared. Supporting these contributions is a detailed discussion of MOP landscape and general test suite issues, and presentation of a new theorem defining the structural limitations of an MOP’s global optimum. This paper also discusses high-performance computer software deterministically computing an MOP’s Pareto front at a given computational resolution.

Evolutionary algorithms for reinforcement learning

Abstract: There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications.

An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends

Abstract: This paper reviews some of the most popular evolutionary multiobjective optimization techniques currently reported in the literature, indicating some of their main applications, their advantages, disadvantages, and degree of applicability. Finally, some of the most promising areas of future research are briefly discussed.

Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming

Abstract: Genesis motivation prediction experiments pattern recognition and classification control system design extension of early evolutionary programming concepts competitive goal-seeking some implications diversification two-person gaming against nonminimax players coevolution, pursuit and evasion modeling time series pattern recognition simulated ecosystems and the nature of intelligence sequence induction with deterministic automata revising and extending early evolutionary programming routing problems comparing crossover, inversion, and mutation specialisations finding structure in data self-adaptation evolving neural networks evolving S-expression and multiple interacting programs games other applications

Three ways to grow designs: a comparison of embryogenies for an evolutionary design problem

Abstract: This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes within evolutionary systems. Following a summary of the significant features of embryogenies, the three main types of embryogenies in Evolutionary Computation are then identified and explained: external, explicit and implicit. An experimental comparison between these three different embryogenies and an evolutionary algorithm with no embryogeny is performed. The problem set to the four evolutionary systems is to evolve tessellating tiles. In order to assess the scalability of the embryogenies, the problem is increased in difficulty by enlarging the size of tiles to be evolved. The results are surprising, with the implicit embryogeny outperforming all other techniques by showing no significant increase in the size of the genotypes or decrease in accuracy of evolution as the scale of the problem is increased.

An empirical study of the efficiency of learning boolean functions using a Cartesian Genetic Programming approach

Abstract: A new form of Genetic Programming (GP) called Cartesian Genetic Programming (CGP) is proposed in which programs are represented by linear integer chromosomes in the form of connections and functionalities of a rectangular array of primitive functions. The effectiveness of this approach is investigated for boolean even-parity functions (3,4,5), and the 2-bit multiplier. The minimum number of evaluations required to give a 0.99 probability of evolving a target function is used to measure the efficiency of the new approach. It is found that extremely low populations are most effective. A simple probabilistic hillclimber (PH) is devised which proves to be even more effective. For these boolean functions either method appears to be much more efficient than the GP and Evolutionary Programming (EP) methods reported. The efficacy of the PH suggests that boolean function learning may not be an appropriate problem for testing the effectiveness of GP and EP.

Evolution, neural networks, games, and intelligence

Abstract: Mathematical games provide a framework for studying intelligent behavior in models of real-world settings or restricted domains. The obstacle comes in choosing the appropriate representation and learning algorithm. Neural networks and evolutionary algorithms provide useful means for addressing these issues. This paper describes efforts to hybridize neural and evolutionary computation to learn appropriate strategies in zero- and nonzero-sum games, including the iterated prisoner's dilemma, tic-tac-toe, and checkers. With respect to checkers, the evolutionary algorithm was able to discover a neural network that can be used to play at a near-expert level without injecting expert knowledge about how to play the game. The implications of evolutionary learning with respect to machine intelligence are also discussed. It is argued that evolution provides the framework for explaining naturally occurring intelligent entities and can be used to design machines that are also capable of intelligent behavior.

Evolutionary computation : theory and applications

Abstract: An introduction to evolutionary computation evolutionary algorithms as search algorithms theoretical analysis of evolutionary algorithms advanced search operators in evolutionary algorithms parallel evolutionary algorithms a comparison of simulated annealing and an evolutionary algorithm on traveling salesman problems power system design and management by evolutionary algorithms telecommunications network design and management by evolutionary algorithms an optimization tool based on evolutionary algorithms the evolution of artificial neural network architectures an experimental study of generalization in evolutionary learning an evolutionary approach to the N-person prisoner's dilemma game automated design and generalisation of heuristics high-order credit assignment in classifier systems.

Evolutionary programming based optimal power flow algorithm

Abstract: Summary form only given. This paper develops an evolutionary programming (EP) based optimal power flow (OPF) solution algorithm which makes use of an EP load flow. Solution acceleration concepts are implemented which improve the basic EP algorithm. This acceleration is implemented using the gradient information obtained using the steepest descent method to perform a local search. The method is capable of determining the global optimum solution to the OPF for a range of constraints and objective functions. The algorithm is not sensitive to starting points and is capable of handling nonconvex generator cost curves. The performances of the algorithm when applied to the IEEE 30-bus test system under different generator input-output curves are presented.

Searching for optima in non-stationary environments

Abstract: Application of evolutionary algorithms to non-stationary problems is the subject of research discussed. We extended evolutionary algorithm by two mechanisms dedicated to non-stationary optimization: redundant genetic memory structures and a particular diversity maintenance technique-random immigrants mechanism. We made experiments with evolutionary optimization employing these two mechanisms (separately and together); the results of experiments are discussed and some observations are made.

Using evolutionary programming and minimum description length principle for data mining of Bayesian networks

Abstract: We have developed a new approach to learning Bayesian network structures based on the minimum description length (MDL) principle and evolutionary programming. It employs a MDL metric, which is founded on information theory, and integrates a knowledge-guided genetic operator for the optimization in the search process.

Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques

Abstract: Real-world electrical engineering problems can take advantage of the last Data Analysis methodologies. In this paper we will show that Genetic Fuzzy Rule-Based Systems and Genetic Programming techniques are good choices for tackling with some practical modeling problems. We claim that both evolutionary processes may produce good numerical results while providing us with a model that can be interpreted by a human being. We will analyze in detail the characteristics of these two methods and we will compare them to the some of the most popular classical statistical modeling methods and neural networks.

Aspects of Evolutionary Design by Computers

Abstract: This paper examines the four main types of Evolutionary Design by computers: Evolutionary Design Optimisation, Evolutionary Art, Evolutionary Artificial Life Forms and Creative Evolutionary Design Definitions for all four areas are provided A review of current work in each of these areas is given, with examples of the types of applications that have been tackled The different properties and requirements of each are examined Descriptions of typical representations and evolutionary algorithms are provided and examples of designs evolved using these techniques are shown The paper then discusses how the boundaries of these areas are beginning to merge, resulting in four new ‘overlapping’ types of Evolutionary Design: Integral Evolutionary Design, Artificial Life Based Evolutionary Design, Aesthetic Evolutionary AL and Aesthetic Evolutionary Design Finally, the last part of the paper discusses some common problems faced by creators of Evolutionary Design systems, including: interdependent elements in designs, epistasis, and constraint handling

Multiple sequence alignment using evolutionary programming

Abstract: Multiple sequence alignment can be used as a tool for the identification of common structure in an ordered string of nucleotides (in DNA or RNA) or amino acids (in proteins). Current multiple sequence alignment algorithms work well for sequences with high similarity but do not scale well when either the length or number of the sequences is large or if the similarity is low. The focus of the paper is to develop an evolutionary programming (EP) algorithm for multiple sequence alignment. An EP method with representation specific variation operators is proposed and tested on several data sets. Comparisons to other algorithms suggests that this algorithm is well suited to the multiple sequence alignment problem.

Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, GE

Abstract: From the Publisher: Genetic algorithms (GA) and evolution strategies (ES) are relatively new stochastic based techniques for solving engineering problems on computers. GA and ES are based on a loose biological analogy: evolutionary theory (mutation, crossover, selection, survival of the fittest). Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming. In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. This book presents state of the art lectures delivered by international academic and industrial experts in the field of evolutionary computing. It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics.

On the Analysis of Evolutionary Algorithms - A Proof That Crossover Really Can Help

Abstract: There is a lot of experimental evidence that crossover is, for some functions, an essential operator of evolutionary algorithms. Nevertheless, it was an open problem to prove for some function that an evolutionary algorithm using crossover is essentially more efficient than evolutionary algorithms without crossover. In this paper, such an example is presented and its properties are proved.