Showing papers on "Evolutionary programming published in 1998"

Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications

Abstract: 1 Genetic Programming as Machine Learning 2 Genetic Programming and Biology 3 Computer Science and Mathematical Basics 4 Genetic Programming as Evolutionary Computation 5 Basic ConceptsThe Foundation 6 CrossoverThe Center of the Storm 7 Genetic Programming and Emergent Order 8 AnalysisImproving Genetic Programming with Statistics 9 Different Varieties of Genetic Programming 10 Advanced Genetic Programming 11 ImplementationMaking Genetic Programming Work 12 Applications of Genetic Programming 13 Summary and Perspectives A Printed and Recorded Resources B Information Available on the Internet C GP Software D Events

Evolutionarily singular strategies and the adaptive growth and branching of the evolutionary tree

Abstract: Summary We present a general framework for modelling adaptive trait dynamics in which we integrate various concepts and techniques from modern ESS-theory The concept of evolutionarily singular strategies is introduced as a generalization of the ESS-concept We give a full classification of the singular strategies in terms of ESSstability, convergence stability, the ability of the singular strategy to invade other populations if initially rare itself, and the possibility of protected dimorphisms occurring within the singular strategy’s neighbourhood Of particular interest is a type of singular strategy that is an evolutionary attractor from a great distance, but once in its neighbourhood a population becomes dimorphic and undergoes disruptive selection leading to evolutionary branching Modelling the adaptive growth and branching of the evolutionary tree can thus be considered as a major application of the framework A haploid version of Levene’s ‘soft selection’ model is developed as a specific example to demonstrate evolutionary dynamics and branching in monomorphic and polymorphic populations

An evolutionary algorithm for multiobjective optimization: the strength Pareto approach

Abstract: Evolutionary algorithms EA have proved to be well suited for optimization prob lems with multiple objectives Due to their inherent parallelism they are able to capture a number of solutions concurrently in a single run In this report we propose a new evolutionary approach to multicriteria optimization the Strength Pareto Evolutionary Algorithm SPEA It combines various features of previous multiobjective EAs in a unique manner and is characterized as follows a besides the population a set of individuals is maintained which contains the Pareto optimal solutions generated so far b this set is used to evaluate the tness of an individual according to the Pareto dominance relationship c unlike the commonly used tness sharing population diversity is preserved on basis of Pareto dominance rather than distance d a clustering method is incorporated to reduce the Pareto set without destroying its characteristics The proof of principle results on two problems suggest that SPEA is very e ective in sampling from along the entire Pareto optimal front and distributing the generated solutions over the tradeo surface Moreover we compare SPEA with four other multiobjective EAs as well as a single objective EA and a random search method in application to an extended knapsack problem Regarding two complementary quantitative measures SPEA outperforms the other approaches at a wide margin on this test problem Finally a number of suggestions for extension and application of the new algorithm are discussed

On economic applications of evolutionary game theory

Abstract: Evolutionary games have considerable unrealized potential for modeling substantive economic issues. They promise richer predictions than orthodox game models but often require more extensive specifications. This paper exposits the specification of evolutionary game models and classifies the possible asymptotic behavior for one and two dimensional models.

Optimal reactive power planning using evolutionary algorithms: a comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming

Abstract: This paper presents a comparative study for three evolutionary algorithms (EAs) to the optimal reactive power planning (ORPP) problem: evolutionary programming, evolutionary strategy, and genetic algorithm. The ORPP problem is decomposed into P- and Q-optimization modules, and each module is optimized by the EAs in an iterative manner to obtain the global solution. The EA methods for the ORPP problem are evaluated against the IEEE 30-bus system as a common testbed, and the results are compared against each other and with those of linear programming.

Combining mutation operators in evolutionary programming

Abstract: Traditional investigations with evolutionary programming for continuous parameter optimization problems have used a single mutation operator with a parametrized probability density function (PDF), typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate PDFs of varying shapes could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination of Gaussian and Cauchy mutations is proposed. Simulations indicate that both the adaptive and nonadaptive versions of this operator are capable of producing solutions that are statistically as good as, or better, than those produced when using Gaussian or Cauchy mutations alone.

Evolutionary-programming-based algorithm for environmentally-constrained economic dispatch

Abstract: This paper develops an efficient and reliable evolutionary-programming-based algorithm for solving the environmentally constrained economic dispatch (ECED) problem. The algorithm can deal with load demand specifications in multiple intervals of the generation scheduling horizon. In the paper, the principal components of the evolutionary-programming-based ECED algorithm are presented. Solution acceleration techniques in the algorithm which enhance the speed and robustness of the algorithm are developed. The power and usefulness of the algorithm is demonstrated through its application to a test system.

Finite Markov chain results in evolutionary computation: a tour d'horizon

Abstract: The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and finite time behavior of evolutionary algorithms with finite search spaces and discrete time scale. Results on evolutionary algorithms beyond finite space and discrete time are also presented but with reduced elaboration.

Advances in Soft Computing: Engineering Design and Manufacturing

Abstract: 1: Keynote Papers.- The NIST Design Repository Project.- Evolving Connectionist and Fuzzy-Connectionist Systems for On-line Adaptive Decision Making and Control.- Recent New Development in Evolutionary Programming.- Emotional Image Retrieval with Interactive Evolutionary Computation.- 2: Design Support Systems.- Using Genetic Algorithms to Encourage Engineering Design Creativity.- Abduction Problem in Probabilistic Constraint Logic Programming.- Aspects of Evolutionary Design by Computers.- Surface Optimisation within the CAD/CAM Environment using Genetic Algorithms.- 3: Intelligent Control.- Adaptive Sugeno Fuzzy Control: A Case Study.- An Experimental and Comparative Study of Fuzzy PID Control Structures.- An Accurate COG Defuzzifier Design Using the Coadaptation of Learning and Evolution.- A Multiagent Intelligent Control System for Glass Industry.- Predictive Control Using Fuzzy Models.- Evolutionary Design of a Helicopter Autopilot.- Decomposition of a Fuzzy Controller Based on Inference Break-up Method.- 4: Identification and Modelling.- Experimental Evaluation of Intelligent Identification Algorithms Applied to a Wind Tunnel Process.- Improvement of Membership Function Identification Method in Usability and Precision.- General Parameter Radial Basis Function Neural Network Based Adaptive Fuzzy Systems.- Uneven Division of Input Spaces for Hierarchical Fuzzy Modeling.- Ensembles of Evolutionary created Artificial Neural Networks and Nearest Neighbour Classifiers.- 5: Data Mining.- Application of Multi-dimensional Fuzzy Analysis to Decision Making.- Information-Theoretic Fuzzy Approach to Knowledge Discovery in Databases.- Intelligent Electronic Catalogs for Sales Support - Introducing Case-Based Reasoning Techniques to on-line Product Selection Applications.- A Genetic Algorithm for Generalized Rule Induction.- 6: Optimisation.- Multiobjective Optimization by Nessy Algorithm.- The Scout Algorithm applied to the Maximum Clique Problem.- Unconstrained Optimization Using Genetic Box Search.- Improvement of Simple Genetic Algorithm for Solving the Uncapacitated Warehouse Location Problem.- Optimizing Neural Networks for Time Series Prediction.- 7: Optimisation for Industrial Applications.- Maximum Entropy Image Restoration by Evolutionary Algorithm.- The Finite Element Method and Soft Computing.- A Tabu Search Approach for the Tool Assignment and Machine Loading Problem in Flexible Manufacturing Systems.- Investigating Evolutionary Optimisation of Constrained Functions to Capture Shape Descriptions from Range Data.- Optimal Selection of Pressure Vessels.- 8: New Topics in EA Basics.- Simulation of Baldwin Effect and Dawkins Memes by Genetic Algorithm.- Approach to Structure Synthesis by Genetic Algorithms.- A Study of Altruism by Genetic Algorithm.- The Bivariate Marginal Distribution Algorithm.- 9: New Frontier for Soft Computing.- Granular Computing using Neighborhood Systems.- Toward Fuzziness in Natural Language Processing.- A New Approach to Acquisition of Comprehensible Fuzzy Rules.- Zero-Point Probability for Linear Source Separation.- Code Optimization for DNA Computing of Maximal Cliques.- 10: Summary of Tutorials.- On Line Tutorials on Evolutionary Computing.- Fuzzy Control Tutorial.- 11: Summary of Discussion.- 11: Summary of Discussion.- Keyword Index.- List of Reviewers.

On a multi-objective evolutionary algorithm and its convergence to the Pareto set

Abstract: Although there are many versions of evolutionary algorithms that are tailored to multi-criteria optimization, theoretical results are apparently not yet available. In this paper, it is shown that results known from the theory of evolutionary algorithms in case of single-criterion optimization do not carry over to the multi-criterion case. At first, three different step size rules are investigated numerically for a selected problem with two conflicting objectives. The empirical results obtained by these experiments lead to the observation that only one of these step size rules may have the property to ensure convergence to the Pareto set. A theoretical analysis finally shows that a special version of an evolutionary algorithm with this step size rule converges with probability one to the Pareto set for the test problem under consideration.

An Introduction to Evolutionary Computation

Abstract: This chapter contains sections titled: References An Introduction to Simulated Evolutionary Optimization Evolutionary Computation: Comments on the History and Current State

A new short-term load forecasting approach using self-organizing fuzzy ARMAX models

Abstract: This paper proposes a new self-organizing model of fuzzy autoregressive moving average with exogenous input variables (FARMAX) for one day ahead hourly load forecasting of power systems To achieve the purpose of self-organizing the FARMAX model, identification of the fuzzy model is formulated as a combinatorial optimization problem Then a combined use of heuristics and evolutionary programming (EP) scheme is relied on to solve the problem of determining optimal number of input variables, best partition of fuzzy spaces and associated fuzzy membership functions The proposed approach is verified by using diverse types of practical load and weather data for Taiwan Power (Taipower) systems Comparisons are made of forecasting errors with the existing ARMAX model implemented by the commercial SAS package and an artificial neural networks (ANNs) method

Intelligent System Applications in Power Engineering: Evolutionary Programming and Neural Networks

Abstract: From the Publisher: Evolutionary programming is the most novel of computational intelligence techniques currently under development for optimization applications in electric power systems. Together with artificial neural network techniques, this from of programming provides the basis for an intelligent system capable of providing innovative solutions for planning, control, protection and operation problems in the fast-changing electric power industry environment. This work provides a solid and thorough treatment of evolutionary programming and neural network applications to power system optimization problems.

On the behavior of evolutionary algorithms in dynamic environments

Abstract: The capability of evolution strategies and evolutionary programming to track the optimum in simple dynamic environments is investigated for different types of dynamics, update frequencies, and displacement strengths. Experimental results are reported for a (15100)-evolution strategy with lognormal self-adaptation, a standard evolutionary programming algorithm with multiplicative self-adaptation rule, and an evolutionary programming algorithm with lognormal self-adaptation. The evolution strategy and lognormal evolutionary programming prove their capability to track the dynamic optimum, while evolutionary programming with multiplicative self-adaptation rule fails in the dynamic environment. These results support the conclusions that the choice of a particular mechanism for self-adaptation can be critical in dynamic environments, and the lognormal rule utilized in evolution strategies is well suited for such kind of problems.

Evolutionary Body Building: Adaptive Physical Designs for Robots

Abstract: Creating artificial life forms through evolutionary robotics faces a "chicken and egg" problem: Learning to control a complex body is dominated by problems specific to its sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain.The idea of coevolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has usually resulted in virtual entities that are not buildable, while embodied evolution in actual robotics is constrained by the slow pace of real time.The work we present takes a step in addressing the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of elementary components that stick together. Evolution takes place in a simulator that computes forces and stresses and predicts stability of three-dimensional brick structures. The final printout of our program is a schematic assembly, which is then built physically. We demonstrate the functionality of this approach to robot body building with many evolved artifacts.

Caep: an evolution-based tool for real-valued function optimization using cultural algorithms

Abstract: Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process within cultural systems. In particular, we compare various approaches that use normative and situational knowledge in different ways to guide the function optimization process. The results in this study demonstrate that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in Evolutionary Programming can produce substantial performance improvements over population-only systems as expressed in terms of (1) systems success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of 34 function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depend on the problem's functional landscape. In addition, it was found that the same held true for the population-only self-adaptive EP systems. Each level of self-adaptation (component, individual, and population) outperformed the others for problems with particular landscape features.

Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Processes

Abstract: This paper attempts to illustrate the importance of a coherent behavioural interpretation in applying evolutionary algorithms like Genetic Algorithms and Genetic Programming to the modelling of social processes. It summarises and draws out the implications of the Neo-Darwinian Synthesis for processes of social evolution and then discusses the extent to which evolutionary algorithms capture the aspects of biological evolution which are relevant to social processes. The paper uses several recent papers in the field as case studies, discussing more and less successful uses of evolutionary algorithms in social science. The key aspects of evolution discussed in the paper are that it is dependent on relative rather than absolute fitness, it does not require global knowledge or a system level teleology, it avoids the credit assignment problem, it does not exclude Lamarckian inheritance and it is both progressive and open ended.

Adaptive Computing in Design and Manufacture

Abstract: Chapter 1 Plenary Papers Pareto Solutions of Multipoint Design of Supersonic Wings Using Evolutionary Algorithms Notes on Design Through Artificial Evolution: Opportunities and Algorithms Testing, Evaluation and Performance of Optimisation and Learning Systems From Evolutionary Computation to Natural Computation Chapter 2 Engineering Design ApplicationsExperiences with Hybrid Evolutionary/Local Optimisation for Process Design The Development of a Grid-based Engineering Design Problem Solving EnvironmentA Hybrid Search Technique for Inverse Transient Analysis in Water Distribution SystemsOptimisation of Thermal Power Plant Designs: A Graph-based Adaptive Search Approach Genetic Algorithm Search for Stent Design Improvements A Multi-objective Optimisation Approach for the Conceptual Design of Frame Structures Multi-Objective Evolutionary Topological Optimum Design Better Surface Intersections by Constrained EvolutionInverse Identification of Boundary Constants for Electronic Packages Using Modified Micro-genetic Algorithm and the Reduced-basis Method Extrinsic Evolution of Finite State Machines Chapter 3 Manufacturing Processes Neural Computing Approach to Shape Change Estimation in Hot Isostatic PressingMulti-criterion Tackling Bottleneck Machines and Exceptional Parts in Cell Formation Using Genetic Algorithms A New Approach to Packing Non-Convex Polygons Using the No Fit Polygon and Meta-Heuristic and Evolutionary Algorithms Chapter 4 System/Process Control Evolutionary Multi-criteria Optimisation for Improved Design of Optimal Control Systems Explorations in Fuzzy Classifier System Architectures Evolving Temporal Rules with the Delayed Action Classifier System - Analysis and New Results Adaptive Image Segmentation Based on Visual Interactive Feedback LearningChapter 5 Strategy/Algorithm DevelopmentAdapting Problem Specifications and Design Solutions Using Co-evolution Handling Constraints in Genetic Algorithms Using Dominance-based TournamentsThe Optimisation of Multi-variate Robust Design CriteriaLearning from Experience: Case Injected Genetic Algorithm Design of Combinational Logic Circuits Constrained Optimisation with the Fuzzy Clustering Evolution StrategyConstrained Optimisation Using an Evolutionary Programming-based Cultural Algorithm A Data Mining Tool Using an Intelligent Processing System with a Clustering Application Chapter 6 Multiple Objectives, Preferences and Agent-Support Full Elite Sets for Multi-objective Optimisation Agent-based Support within an Interactive Evolutionary Design SystemA Multi-agent Architecture for Business Process Management Adapts to Unreliable Performance Real-time Co-ordinated Scheduling Using a Genetic Algorithm

Evolving the Topology and the Weights of Neural Networks Using a Dual Representation

Abstract: Evolutionary computation is a class of global search techniques based on the learning process of a population of potential solutions to a given problem, that has been successfully applied to a variety of problems. In this paper a new approach to the construction of neural networks based on evolutionary computation is presented. A linear chromosome combined to a graph representation of the network are used by genetic operators, which allow the evolution of the architecture and the weights simultaneously without the need of local weight optimization. This paper describes the approach, the operators and reports results of the application of this technique to several binary classification problems.

Exploring multidimensional landscapes without a map

Abstract: A discussion of methodologies for nonlinear geophysical inverse problems is presented Geophysical inverse problems are often posed as optimization problems in a finite-dimensional parameter space An Earth model is usually described by a set of parameters representing one or more geophysical properties (eg the speed with which seismic waves travel through the Earth's interior) Earth models are sought by minimizing the discrepancies between observation and predictions from the model, possibly, together with some regularizing constraint The resulting optimization problem is usually nonlinear and often highly so, which may lead to multiple minima in the misfit landscape Global (stochastic) optimization methods have become popular in the past decade A discussion of simulated annealing, genetic algorithms and evolutionary programming methods is presented in the geophysical context Less attention has been paid to assessing how well constrained, or resolved, individual parameters are Often this problem is poorly posed A new class of method is presented which offers potential in both the optimization and the `error analysis' stage of the inversion This approach uses concepts from the field of computational geometry The search algorithm described here does not appear to be practical in problems with dimension much greater than 10

Evolutionary search for low autocorrelated binary sequences

Abstract: The search for low autocorrelated binary sequences is a classical example of a discrete frustrated optimization problem. We demonstrate the efficiency of a class of evolutionary algorithms to tackle the problem. A suitable mutation operator using a preselection scheme is constructed, and the optimal parameters of the strategy are determined.

An overview of parameter control methods by self-adaption in evolutionary algorithms

Abstract: The principle of self-adaptation in evolutionary algorithms is an important mechanism for controlling the strategy parameters of such algorithms by evolving parameter values in analogy with the usual evolution of object variables. To facilitate evolution of strategy parameters, they are incorporated into the representation of individuals and are subject to the evolutionary variation operators in a similar way as the object variables. This survey paper provides an overview of the existing techniques for the self-adaptation of strategy parameters related to mutation and recombination operators, indicating that the principle works under a variety of conditions regarding the search space of the underlying optimization problem and the method used for the variation of strategy parameters. Although a number of open questions remain, self-adaptation is identified as a generally applicable, robust and efficient method for parameter control in evolutionary algorithms.

Genetic algorithms and evolution strategy in engineering and computer science : recent advances and industrial applications

Abstract: Artificial Evolution: How and Why? (H.-P Schwefel & T. Bdck) Adaptive Niching via Coevolutionary Sharing (D. Goldberg & L. Wang) Representation Issues in Neighbourhood Search and Evolutionary Algorithms (D. Whitley, et al.) Gene Expression: The Missing Link in Evolutionary Computation (H. Kargupta) Immunized Artificial Systems Concepts and Applications (K. Krishnakumar & J. Neidhoefer) Designing Electronic Circuits Using Eolutionary Algorithms Arithmetic Circuits: A Case Study (J. Miller, et al.) Evolutionary Computing for Conceptual and Detailed Design (I. Parmee) Cam Shape Optimization by Genetic Algorithms (J. Alander & J. Lampinen) Evolutionary Algorithms: Applications at the Informatik Center Dortmund (T. Bdck, et al.) Evolutionary Learning Processes for Data Analysis in Electrical Engineering Applications (O. Cordsn, et al.) Ga Multiple Objective Optimization Strategies for Electromagnetic Backscattering (J. Piriaux, et al.) Pareto Genetic Algorithm for Aerodynamic Design Using the Navier-Stokes Equations (S. Obayashi) GA Coupled with Computationally Expensive Simulations: Tools to Improve Efficiency (G. Poloni & V. Pediroda) Coupling Genetic Algorithms and Gradient Based Optimization Techniques (D. Quagliarella & A. Vicini) Evolutionary Synthesis of Control Policies for Manufacturing Systems (B. Porter) Parametric and Non-Parametric Identification of Macro-Mechanical Models (M. Sebag, et al.) Evolutionary Mobile Robotics (D. Floreano) Nonlinear System Identification by Means of Evolutionary Optimised Neural Networks (I. De Falco).

Your brains and my beauty: parent matching for constrained optimisation

Abstract: During the last few years, several methods have been proposed for handling constraints by evolutionary algorithms for parameter optimisation problems. These methods include those based on penalty functions, preservation of feasibility, decoders and repair algorithms, as well as some hybrid techniques. Most of these techniques have serious drawbacks (some of them may return infeasible solutions, others require many additional parameters, etc.). Moreover, none of these techniques has utilized knowledge about which constraints are satisfied and which are not. In this paper, we introduce a new element to evolutionary algorithms for constrained parameter optimization problems: the parent matching mechanism. The preliminary results show that the proposed technique works very well on selected test cases.

Evolutionary algorithms for VLSI CAD

Abstract: Preface. Part I: Basic Principles. 1. Introduction. 2. Evolutionary Algorithms. 3. Characteristics of Problem Instances. 4. Performance Evaluation. Part II: Practice. 5. Implementation. 6. Applications of EAs. 7. Heuristic Learning. 8. Conclusions. References. Index.

An evolutionary algorithm for the optimal design of induction motors

Abstract: This paper describes the application of an evolutionary algorithm to the design of induction motors. It is shown that the use of an evolutionary algorithm offers advantages over other approaches. These include a high rate of global convergence and the ability to handle discrete variables.

A rigorous complexity analysis of the (1+1) evolutionary algorithm for linear functions with Boolean inputs

Abstract: Evolutionary algorithms (EAs) are heuristic randomized algorithms which, by many impressive experiments, have been proven to behave quite well for optimization problems of various kinds. In this paper, a rigorous complexity analysis of the (1+1) evolutionary algorithm for linear functions with Boolean inputs is given. The analysis is carried out for different mutation rates. The main contribution of the paper is not the result that the expected run time of the (1+1) evolutionary algorithm is at most /spl Theta/(n ln n) for linear functions with n variables, but the presentation of methods showing how this result can be proven rigorously.