Showing papers on "Evolutionary programming published in 2007"

Parameter Control in Evolutionary Algorithms

Abstract: The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classification of different approaches based on a number of complementary features, and pay special attention to setting parameters on-the-fly. This has the potential of adjusting the algorithm to the problem while solving the problem. This paper is intended to present a survey rather than a set of prescriptive details for implementing an EA for a particular type of problem. For this reason we have chosen to interleave a number of examples throughout the text. Thus we hope to both clarify the points we wish to raise as we present them, and also to give the reader a feel for some of the many possibilities available for controlling different parameters. © Springer-Verlag Berlin Heidelberg 2007.

Evolutionary computation: a unified approach

Abstract: The field of Evolutionary Computation has experienced tremendous growth over the past 20 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to se the relationships between them, assess strengths and weaknesses, and make good choices for new application areas. This tutorial is intended to give an overview of a general EC framework that can help compare and contrast approaches, encourages crossbreeding, and facilitates intelligent design choices. The use of this framework is then illustrated by showing how traditional EAs can be compared and contrasted with it, and how new EAs can be effectively designed using it. Finally, the framework is used to identify some important open issues that need further research.

Evolutionary computation: a unified approach

Abstract: While Lawrence Fogel, John Holland, Ingo Rechenberg and others were the undoubted pioneers of the field we now know as evolutionary algorithms (EA), or evolutionary computation (EC), Ken De Jong’s doctoral thesis of 1975 deserves much of the credit for firing the enthusiasm of several research communities in the practical exploration of these methods. Moreover, as he has taken a very active part in the development of the field through the last 30 years, there could scarcely be anyone better placed to write a book on evolutionary computation. As the subtitle of his book promises, De Jong takes a unified approach. His first 4 chapters carefully explain and differentiate, whilst putting in their historical context, the common aspects of different EC paradigms (evolutionary programming—EP, evolution strategies—ES and genetic algorithms—GA). Chapters 1–4 use clear examples, rather than too many mathematical symbols. They form a truly superb introduction. Any novice coming to EC should come away with an excellent grasp of the basics. In chapter 5 he discusses the different uses to which EAs have been put as problem-solvers. The greater part is devoted to optimization (OPT-EA), with shorter sections on search, machine learning, and automated programming. There is a final, very brief, section on adaptive EAs. In the optimization part, considerable care is taken in the organisation of his material—again, presumably, with the novice in mind. Chapter 6 is the longest, and focuses on EC theory. De Jong carefully builds up a picture of the influences of selection, mutation and recombination on the behaviour of EAs. If you are expecting theory in the sense of a comprehensive, general model with well-understood effects, you will be disappointed. There are equations, but the argument is in fact founded on a series of experiments, whose results are displayed in a series of graphs. That is not to say that the insights gained are incorrect, or

Parameter Setting in Evolutionary Algorithms

Abstract: One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.

Evolutionary computation in dynamic and uncertain environments

Abstract: This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified ...

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Abstract: Summary. Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.

Self-Adaptation in Evolutionary Algorithms

Abstract: Summary. In this chapter, we will give an overview over self-adaptive methods in evolutionary algorithms. Self-adaptation in its purest meaning is a state-of-the-art method to adjust the setting of control parameters. It is called self-adaptive because the algorithm controls the setting of these parameters itself – embedding them into an individual’s genome and evolving them. We will start with a short history of adaptation methods. The section is followed by a presentation of classification schemes for adaptation rules. Afterwards, we will review empirical and theoretical research of self-adaptation methods applied in genetic algorithms, evolutionary programming, and evolution strategies.

JCLEC: a Java framework for evolutionary computation

Abstract: In this paper we describe JCLEC, a Java software system for the development of evolutionary computation applications. This system has been designed as a framework, applying design patterns to maximize its reusability and adaptability to new paradigms with a minimum of programming effort. JCLEC architecture comprises three main modules: the core contains all abstract type definitions and their implementation; experiments runner is a scripting environment to run algorithms in batch mode; finally, GenLab is a graphical user interface that allows users to configure an algorithm, to execute it interactively and to visualize the results obtained. The use of JCLEC system is illustrated though the analysis of one case study: the resolution of the 0/1 knapsack problem by means of evolutionary algorithms.

Applications of multi-objective evolutionary algorithms in economics and finance: A survey

Abstract: This paper provides a state-of-the-art survey of applications of multi-objective evolutionary algorithms in economics and finance reported in the specialized literature. A taxonomy of applications within this area is proposed, and a brief review of the most representative research reported to date is then provided. In the final part of the paper, some potential paths for future research within this area are identified.

Optimal Spinning Reserve for a Wind-Thermal Power System Using EIPSO

Abstract: This paper presents an evolutionary iteration particle swarm optimization (EIPSO) algorithm to solve the nonlinear optimal scheduling problem. A new index called iteration best is incorporated into particle swarm optimization (PSO) to improve the solution quality. The new PSO, named iteration PSO (IPSO), is embedded into evolutionary programming (EP) to further improve the computational efficiency. The EIPSO is then applied to solve the optimal spinning reserve for a wind-thermal power system (OSRWT). Results are used to evaluate the effects of wind generation on the spinning reserve selection of a power system. The OSRWT program considers the outage cost as well as the total operation cost of thermal units to evaluate the level of spinning reserve. The up spinning reserve (USR) and down spinning reserve (DSR) are also introduced into the OSRWT problem. The optimal scheduling of spinning reserve was reached while minimizing the sum of total operation cost and outage cost. Two practical power systems are used as numerical examples to test the new algorithm. The feasibility of the new algorithm is demonstrated by the numerical example, and EIPSO solution quality and computational efficiency are compared to those of other algorithms.

Making a Difference to Differential Evolution

Abstract: Differential evolution (DE) and evolutionary programming (EP) are two major algorithms in evolutionary computation They have been applied with success to many real-world numerical optimization problems Neighborhood search (NS) is a main strategy underpinning EPThere have been analyses of different NS operators’ characteristics Although DE might be similar to the evolutionary process in EP, it lacks the relevant concept of neighborhood search In this chapter, DE with neighborhood search (NSDE) is proposed based on the generalization of NS strategy The advantages of NS strategy in DE are analyzed theoretically These analyses mainly focus on the change of search step size and population diversity after using neighborhood search Experimental results have shown that DE with neighborhood search has significant advantages over other existing algorithms on a broad range of different benchmark functions NSDE’s scalability is also evaluated on a number of benchmark problems, whose dimension ranges from 50 to 200

An Evolutionary Algorithm for Polishing Mixed Integer Programming Solutions

Abstract: Evolutionary algorithms adopt a natural-selection analogy, exploiting concepts such as population, combination, mutation, and selection to explore a diverse space of possible solutions to combinatorial optimization problems while, at the same time, retaining desirable properties from known solutions. This paper describes an evolutionary approach to improving solutions to mixed integer programming (MIP) models. We propose coarse-grained approaches to mutating and combining MIP solutions, both built within a large-neighborhood search framework. These techniques are then integrated within a MIP branch-and-bound framework. The resulting solution-polishing heuristic often finds significantly better feasible solutions to very difficult MIP models than do available alternatives. In contrast to most evolutionary algorithms, our polishing heuristic is domain-independent, requiring no structural information about the underlying combinatorial problem, above and beyond the information contained in the original MIP formulation.

Automatic Design of Hierarchical Takagi–Sugeno Type Fuzzy Systems Using Evolutionary Algorithms

Abstract: This paper presents an automatic way of evolving hierarchical Takagi-Sugeno fuzzy systems (TS-FS). The hierarchical structure is evolved using probabilistic incremental program evolution (PIPE) with specific instructions. The fine tuning of the if - then rule's parameters encoded in the structure is accomplished using evolutionary programming (EP). The proposed method interleaves both PIPE and EP optimizations. Starting with random structures and rules' parameters, it first tries to improve the hierarchical structure and then as soon as an improved structure is found, it further fine tunes the rules' parameters. It then goes back to improve the structure and the rules' parameters. This loop continues until a satisfactory solution (hierarchical TS-FS model) is found or a time limit is reached. The proposed hierarchical TS-FS is evaluated using some well known benchmark applications namely identification of nonlinear systems, prediction of the Mackey-Glass chaotic time-series and some classification problems. When compared to other neural networks and fuzzy systems, the developed hierarchical TS-FS exhibits competing results with high accuracy and smaller size of hierarchical architecture.

Evolutionary Programming in Electromagnetic Optimization: A Review

Abstract: In this paper, we review recent advances in evolutionary programming (EP) and its implementation in various antenna, microwave, frequency selective surfaces and RF circuit optimization problems. EP, unlike the other two paradigms of evolutionary computational techniques, namely, genetic algorithms (GA) and evolution strategies (ES), uses a mutation only variation operator where adaptive and/or self-adaptive techniques exist, or can easily be designed, for adapting the parameters of mutation operator during the evolution process. We present the so-called meta-EP and design of its mutation operator, using Gaussian, Cauchy and Poisson mutations as well as a hybrid of these mutations, for continuous, discrete and mixed parameter electromagnetic optimization problems. In addition, an efficient hybrid use of EP, gradient search methods and cluster analysis, as well as a novel hybrid EP-GA algorithm are discussed. Examples presented include optimizations of corrugated horn antennas, multilayered stacked microstrip antennas, Yagi-Uda arrays, partially reflective surfaces, dielectric filters, meander-line polarizer and RF duplexers

A Multiple Objective Approach to Direct Load Control Using an Interactive Evolutionary Algorithm

Abstract: This paper describes the use of an interactive evolutionary algorithm for the identification and selection of direct load control actions in electrical distribution networks. The evolutionary algorithm accommodates a progressive articulation of the decision maker's preferences by changing aspiration or reservation levels used in the fitness assessment of the individuals in the population (load control strategies). Genetic operators have revealed as an adequate way to supply the evolutionary process with relevant information about the search results. Besides contributing to reduce the scope of the search, and thus the computer effort, this also enables the identification of solutions more in accordance with the decision maker's evolving preferences.

How well do multi-objective evolutionary algorithms scale to large problems

Abstract: In spite of large amount of research work in multi- objective evolutionary algorithms, most have evaluated their algorithms on problems with only two to four objectives. Little has been done to understand the performance of the multi- objective evolutionary algorithms on problems with a larger number of objectives. It is unclear whether the conclusions drawn from the experiments on problems with a small number of objectives could be generalised to those with a large number of objectives. In fact, some of our preliminary work [1] has indicated that such generalisation may not be possible. This paper first presents a comprehensive set of experimental studies, which show that the performance of multi-objective evolutionary algorithms, such as NSGA-II and SPEA2, deteriorates substantially as the number of objectives increases. NSGA-II, for example, did not even converge for problems with six or more objectives. This paper analyses why this happens and proposes several new methods to improve the convergence of NSGA-II for problems with a large number of objectives. The proposed methods categorise members of an archive into small groups (non-dominated solutions with or without domination), using dominance relationship between the new and existing members in the archive. New removal strategies are introduced. Our experimental results show that the proposed methods clearly outperform NSGA-II in terms of convergence.

The halt condition in genetic programming

Abstract: In this paper we address the role of divergence and convergence in creative processes, and argue about the need to consider them in Computational Creativity research in the Genetic or Evolutionary Programming paradigm, being one's goal the problem of the Halt Condition in Genetic Programming. Here the candidate solutions are seen as evolutionary logic programs or theories, being the test whether a solution is optimal based on a measure of the quality-of-information carried out by those logical theories or programs. Furthermore, we present Conceptual Blending Theory as being a promising framework for implementing convergence methods within creativity programs, in terms of the logic programming framework.

Dynamic Economic Emission Dispatch Using Evolutionary Programming and Fuzzy Satisfying Method

Abstract: Dynamic economic emission dispatch is an extension of the conventional economic emission dispatch problem that takes into consideration the ramp rate limits of the generators. Evolutionary programming based fuzzy satisfying method has been proposed to determine the optimal noninferior generation schedule. This paper treats economy and emission as competing objectives for optimal dispatch, which requires some form of conflict resolution to arrive at a solution. The multi-objective problem is configured as a minimax problem under the assumption that the decision-maker has fuzzy goals for each of the objective functions. The present algorithm employs evolutionary programming technique in solving this problem. The solution methodology can offer a global or near-global noninferior solution for the decision-maker. Numerical results of a sample test system have been presented to demonstrate the performance and applicability of the proposed method. Results obtained from the proposed method are compared to those obtained by fuzzy satisfying method based on simulated annealing technique.

A versatile quantum-inspired evolutionary algorithm

Abstract: This study points out some weaknesses of existing quantum-inspired evolutionary algorithms (QEA) and explains in particular how hitchhiking phenomena can slow down the discovery of optimal solutions and encourage premature convergence. A new algorithm, called versatile quantum- inspired evolutionary algorithm (vQEA), is proposed. With vQEA, the attractors moving the population through the search space are replaced at every generation without considering their fitness. The new algorithm is much more reactive. It always adapts the search toward the last promising solution found thus leading to a smoother and more efficient exploration. In this paper, vQEA is tested and compared to a classical genetic algorithm CGA and to a QEA on several benchmark problems. Experiments have shown that vQEA performs better than both CGA and QEA in terms of speed and accuracy. It is a highly scalable algorithm as well. Finally, the properties of the vQEA are discussed and compared to estimation of distribution algorithms (EDA).

Evolutionary programming‐based economic dispatch for units with multiple fuel options

Abstract: This paper presents the application of evolutionary programming (EP) to economic dispatch of generators having piecewise quadratic cost functions. The proposed EP algorithm always determines the global or near global optimum without any restrictions on the shape of the cost functions. The method is applied to an example system with piecewise cost functions and the results are compared with those of the recent adaptive Hopfield neural network method. The test results prove that the EP method is simpler and more efficient for solving economic dispatch (ED) problems with multiple cost curves than many existing techniques.

Explicit memory schemes for evolutionary algorithms in dynamic environments

Abstract: Problem optimization in dynamic environments has atrracted a growing interest from the evolutionary computation community in reccent years due to its importance in real world optimization problems. Several approaches have been developed to enhance the performance of evolutionary algorithms for dynamic optimization problems, of which the memory scheme is a major one. This chapter investigates the application of explicit memory schemes for evolutionary algorithms in dynamic environments. Two kinds of explicit memory schemes: direct memory and associative memory, are studied within two classes of evolutionary algorithms: genetic algorithms and univariate marginal distribution algorithms for dynamic optimization problems. Based on a series of systematically constructed dynamic test environments, experiments are carried out to investigate these explicit memory schemes and the performance of direct and associative memory schemes are campared and analysed. The experimental results show the efficiency of the memory schemes for evolutionary algorithms in dynamic environments, especially when the environment changes cyclically. The experimental results also indicate that the effect of the memory schemes depends not only on the dynamic problems and dynamic environments but also on the evolutionary algorithm used.

A New Dynamic Multi-objective Optimization Evolutionary Algorithm

Abstract: Dynamic multi-objective optimization problems are very common in real-world applications. The researches on applying evolutionary algorithm into such problems are attracting more and more researchers. In this paper, a new dynamic multi-objective optimization evolutionary algorithm which utilizes hyper-mutation operator to deal with dynamics and geometrical Pareto selection to deal with multi-objective is introduced. The experimental results show that the performance is satisfactory.

Genetic Programming Theory and Practice IV

Abstract: Contributing Authors.- Preface.- Foreword.- Genetic Programming: Theory and Practice.- Genome-Wide Genetic Analysis Using Genetic Programming: The Critical Need for Expert Knowledge.- Lifting the Curse of Dimensionality.- Genetic Programming for Classifying Cancer Data and Controlling Humanoid Robots.- Boosting Improves Stability and Accuracy of Genetic Programming in Biological Sequence Classification.- Othogonal Evoluton of Teams: A Class of Algorithms for Evolving Teams with Inversely Correlated Errors.- Multidimensional Tags, Cooperative Populations, and Genetic Programming.- Coevolving Fitness Models for Accelerating Evolution and Reducing Evaluations.- Multi-Domain Observations Concerning the Use of Genetic Programming to Automatically Synthesize Human-Competitive Designs for Analog Circuits, Optical Lens Systems, Controllers, Antennas, Mechanical Systems, and Quantum Computing Circuits.- Robust Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data.- Pursuing the Pareto Paradigm: Tournaments, Algorithm Variations and Ordinal Optimization.- Applying Genetic Programming to Reservoir History Matching Problem.- Comparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphs.- Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms.- Phase Transitions in Genetic Programming Search.- Efficient Markov Chain Model of Machine Code Program Execution and Halting.- A Re-examination of a Real World Blood Flow Modeling Problem Using Context-aware Crossover.- Large-Scale, Time-Constrained Symbolic Regression.- Stock Selection: An Innovative Application of Genetic Programming Methodology.- Index.