Showing papers on "Evolutionary programming published in 2000"
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TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
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.
4,867 citations
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15 Apr 2000TL;DR: A neutral search strategy that allows the fittest genotype to be replaced by another equally fit genotype (a neutral genotype) is examined and compared with non-neutral search for the Santa Fe ant problem and the neutral search proves to be much more effective.
Abstract: This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node functions are also separately numbered. The genotype is just a list of node connections and functions. The genotype is then mapped to an indexed graph that can be executed as a program. Evolutionary algorithms are used to evolve the genotype in a symbolic regression problem (sixth order polynomial) and the Santa Fe Ant Trail. The computational effort is calculated for both cases. It is suggested that hit effort is a more reliable measure of computational efficiency. A neutral search strategy that allows the fittest genotype to be replaced by another equally fit genotype (a neutral genotype) is examined and compared with non-neutral search for the Santa Fe ant problem. The neutral search proves to be much more effective.
973 citations
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16 Jul 2000TL;DR: In this article, the authors define and execute a quantitative MOEA performance comparison methodology and present results from its execution with four MOEAs, and describe the results of their experiments.
Abstract: Solving optimization problems with multiple (often conflicting) objectives is generally a quite difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade a multiplicity of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define and execute a quantitative MOEA performance comparison methodology. Almost all comparisons cited in the current literature visually compare algorithmic results, resulting in only relative conclusions. Our methodology gives a basis for absolute conclusions regarding MOEA performance. Selected results from its execution with four MOEAs are presented and described.
481 citations
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20 Nov 2000TL;DR: The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms.
Abstract: Evolutionary Computation 2: Advanced Algorithms and Operators expands upon the basic ideas underlying evolutionary algorithms. The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms. It is intended to be used by individual researchers and students in the expanding field of evolutionary computation.
462 citations
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01 Jan 2000TL;DR: Concepts from the ”forking GA” (a multi-population evolutionary algorithm proposed to find multiple peaks in a multi-modal landscape) are used to enhance search in a dynamic landscape.
Abstract: Time-dependent optimization problems pose a new challenge to evolutionary algorithms, since they not only require a search for the optimum, but also a continuous tracking of the optimum over time. In this paper, we will will use concepts from the ”forking GA” (a multi-population evolutionary algorithm proposed to find multiple peaks in a multi-modal landscape) to enhance search in a dynamic landscape. The algorithm uses a number of smaller populations to track the most promising peaks over time, while a larger parent population is continuously searching for new peaks. We will show that this approach is indeed suitable for dynamic optimization problems by testing it on the recently proposed Moving Peaks Benchmark.
342 citations
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TL;DR: Intelligent hybrid systems that incorporate an integration of two or more of these paradigms and their application in the oil and gas industry are also discussed in these articles.
Abstract: This is the first article of a three-article series on virtual intelligence and its applications in petroleum and natural gas engineering. In addition to discussing artificial neural networks, the series covers evolutionary programming and fuzzy logic. Intelligent hybrid systems that incorporate an integration of two or more of these paradigms and their application in the oil and gas industry are also discussed in these articles. The intended audience is the petroleum professional who is not quite familiar with virtual intelligence but would like to know more about the technology and its potential. Those with a prior understanding of and experience with the technology should also find the articles useful and informative.
307 citations
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16 Jul 2000
TL;DR: The most important preference handling approaches used with evolutionary algorithms, analyzing their advantages and disadvantages are reviewed, and then, some of the potential areas of future research in this discipline are proposed.
Abstract: Despite the relatively high volume of research conducted on evolutionary multiobjective optimization in the last few years. Little attention has been paid to the decision making process that is required to select a final solution to the multiobjective optimization problem at hand. This paper reviews the most important preference handling approaches used with evolutionary algorithms, analyzing their advantages and disadvantages, and then, it proposes some of the potential areas of future research in this discipline.
290 citations
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16 Jul 2000TL;DR: Four abstract evolutionary algorithms for multi-objective optimization and theoretical results that characterize their convergence behavior are presented and it is easy to verify whether or not a particular instantiation of these abstract evolutionary algorithm offers the desired limit behavior.
Abstract: We present four abstract evolutionary algorithms for multi-objective optimization and theoretical results that characterize their convergence behavior. Thanks to these results it is easy to verify whether or not a particular instantiation of these abstract evolutionary algorithms offers the desired limit behavior. Several examples are given.
242 citations
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16 Jul 2000TL;DR: A unified model of multi-objective evolutionary algorithms, in which arbitrary variation and selection operators can be combined as building blocks, including archiving and re-insertion strategies is presented.
Abstract: Though it has been claimed that elitism could improve evolutionary multi-objective search significantly, a thorough and extensive evaluation of its effects is still missing. Guidelines on how elitism could successfully be incorporated have not yet been developed. This paper presents a unified model of multi-objective evolutionary algorithms, in which arbitrary variation and selection operators can be combined as building blocks, including archiving and re-insertion strategies. The presented model enables most specific multi-objective (evolutionary) algorithm to be formulated as an instance of it, which will be demonstrated by simple examples. We further show how elitism can be quantified by the model's parameters and how this allows an easy evaluation of the effect of elitism on different algorithms.
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TL;DR: This paper presents two new tree-generation algorithms for genetic programming and for "strongly typed" genetic programming, a common variant, that are fast, allow the user to request specific tree sizes, and guarantee probabilities of certain nodes appearing in trees.
Abstract: Genetic programming is an evolutionary optimization method that produces functional programs to solve a given task. These programs commonly take the form of trees representing LISP s-expressions, and a typical evolutionary run produces a great many of these trees. For this reason, a good tree-generation algorithm is very important to genetic programming. This paper presents two new tree-generation algorithms for genetic programming and for "strongly typed" genetic programming, a common variant. These algorithms are fast, allow the user to request specific tree sizes, and guarantee probabilities of certain nodes appearing in trees. The paper analyzes these two algorithms, and compares them with traditional and recently proposed approaches.
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10 Jul 2000TL;DR: It is found that the evolutionary algorithm will converge incorrectly if the approximate model has false optima, so two strategies to control the evolution process are introduced and methods to eliminate false minima in neural network training are proposed.
Abstract: The evaluation of the quality of solutions is usually very time-consuming in design optimization. Therefore, time-efficient approximate models can be particularly beneficial for the evaluation when evolutionary algorithms are applied. In this paper, the convergence property of an evolution strategy (ES) with neural network based fitness evaluations is investigated. It is found that the evolutionary algorithm will converge incorrectly if the approximate model has false optima. To address this problem, two strategies to control the evolution process are introduced. In addition, methods to eliminate false minima in neural network training are proposed. The effectiveness of the methods are shown with simulation studies on the Ackley function and the Rosenbrock function.
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01 Aug 2000
TL;DR: This book provides a characterization of the roles that recombination and mutation play in evolutionary algorithms and introduces new theoretical techniques for studying evolutionary algorithms.
Abstract: Despite decades of work in evolutionary algorithms, there remains an uncertainty as to the relative benefits and detriments of using recombination or mutation. This book provides a characterization of the roles that recombination and mutation play in evolutionary algorithms. It integrates important prior work and introduces new theoretical techniques for studying evolutionary algorithms. Consequences of the theory are explored and a novel method for comparing search and optimization algorithms is introduced. The focus allows the book to bridge multiple communities, including evolutionary biologists and population geneticists.
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TL;DR: In this paper, a hybrid evolutionary modeling algorithm is presented to implement the automatic modeling of one-and multi-dimensional dynamic systems, where the main idea of the method is to embed a genetic algorithm in genetic programming where the latter algorithm is employed to discover and optimize the structure of a model, while the former algorithm optimizes its parameters.
Abstract: This paper describes an approach to the evolutionary modeling problem of ordinary differential equations including systems of ordinary differential equations and higher-order differential equations. Hybrid evolutionary modeling algorithms are presented to implement the automatic modeling of one- and multi-dimensional dynamic systems respectively. The main idea of the method is to embed a genetic algorithm in genetic programming where the latter is employed to discover and optimize the structure of a model, while the former is employed to optimize its parameters. A number of practical examples are used to demonstrate the effectiveness of the approach. Experimental results show that the algorithm has some advantages over most available modeling methods.
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TL;DR: An overview of evolutionary computation as applied to problems in the medical domains is provided, outlining the basic workings of six types of evolutionary algorithms: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, classifier systems, and hybrid systems.
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TL;DR: Using five chaotic time series functions, empirically compares a genetic algorithm with backpropagation for training NNs using the chaotic series because of their similarity to economic and financial series found in financial markets.
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19 Apr 2000TL;DR: A distributed hierarchical evolutionary modeling and visualization of empirical data method and machine readable storage medium for creating an empirical modeling system based upon previously acquired data is presented in this article, where the data represents inputs to the systems and corresponding outputs from the system.
Abstract: A distributed hierarchical evolutionary modeling and visualization of empirical data method and machine readable storage medium for creating an empirical modeling system based upon previously acquired data. The data represents inputs to the systems and corresponding outputs from the system. The method and machine readable storage medium utilize an entropy fonction based upon information theory and the principles of thermodynamics to accurately predict system outputs from subsequently acquired inputs. The method and machine readable storage medium identify the most information-rich (i.e., optimum) representation of a data set in order to reveal the underlying order, or structure, of what appears to be a disordered system. Evolutionary programming is one method utilized for identifying the optimum representation of data.
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TL;DR: A coevolutionary method developed for solving constrained optimization problems based on the evolution of two populations with opposite objectives to solve saddle-point problems that provides consistent solutions with better numerical accuracy than other evolutionary methods.
Abstract: This paper introduces a coevolutionary method developed for solving constrained optimization problems. This algorithm is based on the evolution of two populations with opposite objectives to solve saddle-point problems. The augmented Lagrangian approach is taken to transform a constrained optimization problem to a zero-sum game with the saddle point solution. The populations of the parameter vector and the multiplier vector approximate the zero-sum game by a static matrix game, in which the fitness of individuals is determined according to the security strategy of each population group. Selection, recombination, and mutation are done by using the evolutionary mechanism of conventional evolutionary algorithms such as evolution strategies, evolutionary programming, and genetic algorithms. Four benchmark problems are solved to demonstrate that the proposed coevolutionary method provides consistent solutions with better numerical accuracy than other evolutionary methods.
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TL;DR: A terminology and a general framework is given for the description of the main features of any particular evolutionary algorithm, to develop tools that may help understanding the “philosophy” of such methods.
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TL;DR: A novel optimization technique based on evolutionary programming for overcurrent (OC) relay coordination in ring fed distribution networks that can handle the resultant relay operation due to the changes of fault current distribution as a result of circuit breaker operation while the fault still exists.
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01 Dec 2000TL;DR: This work proposes a new method, genetic network programming (GNP), which is composed of plural nodes for agents to execute simple judgment/processing and they are connected with each other to form a network structure.
Abstract: Recently many studies have been made on the automatic design of complex systems using evolutionary optimization techniques such as genetic algorithms (GA), evolution strategy (ES), evolutionary programming (EP) and genetic programming (GP). It is generally recognized that these techniques are very useful for optimizing fairly complex systems such as the generation of intelligent behavior sequences of robots. A new method, genetic network programming (GNP), is proposed in order to acquire these behavior sequences efficiently. GNP is composed of plural nodes for agents to execute simple judgment/processing and they are connected with each other to form a network structure. Agents behave according to the contents of the nodes and their connections in GNP. In order to obtain a better structure, the GNP changes itself using evolutionary optimization techniques.
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16 Jul 2000TL;DR: This research investigates difficulties that occur during the solution of NSP using evolutionary algorithms, in particular genetic algorithms (GA).
Abstract: The nurse scheduling problem (NSPs) represents a difficult class of multi-objective optimisation problems consisting of a number of interfering objectives between the hospitals and individual nurses. The objective of this research is to investigate difficulties that occur during the solution of NSP using evolutionary algorithms, in particular genetic algorithms (GA). As the solution method a population-less cooperative genetic algorithm (CGA) is taken into consideration. Because contrary to competitive GAs, we have to simultaneously deal with the optimization of the fitness of the individual nurses and also optimization of the entire schedule as the final solution to the problem in hand. To confirm the search ability of CGA, first a simplified version of NSP is examined. Later we report a more complex and useful version of the problem. We also compare CGA with another multi-agent evolutionary algorithm using pheromone style communication of real ants. Finally, we report the results of computer simulations acquired throughout the experiments.
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TL;DR: In this article, a new approach to include tie line constraints in multi-area economic dispatch problems by using evolutionary programming (EP) was presented, and the proposed method always finds the global or near global optimum for small and reasonable-sized multi-region economic dispatch.
Abstract: This paper presents a new approach to include tie line constraints in multiarea economic dispatch problems by using evolutionary programming (EP). The proposed method always finds the global or near global optimum for small and reasonable-sized multiarea economic dispatch (MAED) problems. The inclusion of tie line constraints to MAED does not introduce any complexity in the approach. The applicability and validity of the proposed method is shown by implementing it on three example systems - 2, 4, and 14 areas - and their results are compared with those obtained by classical economic dispatch, network flow programming, and dynamic programming methods, respectively. The results show that the proposed method can serve as a potential tool for solving MAED problems.
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01 Oct 2000••
15 Apr 2000TL;DR: An experimental study of a number of common GP test problems finds that an optimal range of values exists, which assists in the choice of population size and in the selection of an appropriate parallel genetic programming model.
Abstract: The parallel execution of several populations in evolutionary algorithms has usually given good results. Nevertheless, researchers have to date drawn conflicting conclusions when using some of the parallel genetic programming models. One aspect of the conflict is population size, since published GP works do not agree about whether to use large or small populations. This paper presents an experimental study of a number of common GP test problems. Via our experiments, we discovered that an optimal range of values exists. This assists us in our choice of population size and in the selection of an appropriate parallel genetic programming model. Finding efficient parameters helps us to speed up our search for solutions. At the same time, it allows us to locate features that are common to parallel genetic programming and the classic genetic programming technique.
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TL;DR: A generalization of common approaches like evolution strategies is considered: a multiobjective evolutionary algorithm (MOEA) for analyzing decision problems with alternatives taken from a real-valued vector space and evaluated according to several objective functions.
Abstract: Since the 60s, several approaches (genetic algorithms, evolution strategies etc.) have been developed which apply evolutionary concepts for simulation and optimization purposes. Also in the area of multiobjective programming, such approaches (mainly genetic algorithms) have already been used (Evolutionary Computation 3(1), 1–16).
In our presentation, we consider a generalization of common approaches like evolution strategies: a multiobjective evolutionary algorithm (MOEA) for analyzing decision problems with alternatives taken from a real-valued vector space and evaluated according to several objective functions. The algorithm is implemented within the Learning Object-Oriented Problem Solver (LOOPS) framework developed by the author. Various test problems are analyzed using the MOEA: (multiobjective) linear programming, convex programming, and global programming. Especially for ‘hard’ problems with disconnected or local efficient regions, the algorithms seems to be a useful tool.
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16 Jul 2000TL;DR: This paper is concerned with the development of an evolutionary programming method, which is functionally, and structurally equivalent to BEP, but still can be used effectively to optimize functions having strong fitness dependency between parents and their offspring.
Abstract: The basic evolutionary programming (BEP) method utilizes individual parent fitness to generate offspring. This is objectionable in many optimization problems, where the fitness value grows rapidly with problem dimensions, and two optimization problems differ by simply a scale factor. This paper is concerned with the development of an evolutionary programming method, which is functionally, and structurally equivalent to BEP, but still can be used effectively to optimize functions having strong fitness dependency between parents and their offspring. In this paper, a fitness-blind mutation (FBM) algorithm has been proposed, and then this is used in conjunction with the BEP mutation operator. The FBM operation has been implemented by taking the standard deviation of the Gaussian variable to vary in proportion to the genotypic distance between the individual parent and the fittest individual, which is defined as a pseudo-global optimum individual in a population pool. Also, the directionality of the random variation has been exploited to improve the probability of getting better solutions. In addition to this, the importance of initial search width for generating the offspring has been established empirically. The effectiveness of the proposed algorithm has been verified on well-established test functions.
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TL;DR: The authors present their approach for knowledge discovery from two specific medical databases, where rules are learned to represent the interesting patterns of the data and Bayesian networks are induced to act as causality relationship models among the attributes.
Abstract: Discusses learning roles and causal structures for capturing patterns and causality relationships. The authors present their approach for knowledge discovery from two specific medical databases. First, rules are learned to represent the interesting patterns of the data. Second, Bayesian networks are induced to act as causality relationship models among the attributes. The Bayesian network learning process is divided into two phases. In the first phase, a discretization policy is learned to discretize the continuous variables, and then Bayesian network structures are induced in the second phase. The authors employ advanced evolutionary algorithms such as generic genetic programming, evolutionary programming, and genetic algorithms to conduct the learning tasks. From the fracture database, they discovered knowledge about the patterns of child fractures. From the scoliosis database, they discovered knowledge about the classification of scoliosis. They also found unexpected rules that led to discovery of errors in the database. These results demonstrate that the knowledge discovery process can find interesting knowledge about the data, which can provide novel clinical knowledge as well as suggest refinements of the existing knowledge.