Showing papers on "Evolutionary programming published in 2004"

Genetic and Evolutionary Computation - GECCO 2004

Abstract: The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004. The 230 revised full papers and 104 poster papers presented were carefully reviewed and selected from 460 submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; artificial immune systems, biological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering.

Evolutionary programming using mutations based on the Levy probability distribution

Abstract: Studies evolutionary programming with mutations based on the Levy probability distribution. The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter /spl alpha/ in the Levy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Levy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation.

Applications of Multi-Objective Evolutionary Algorithms

Abstract: An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach Using a Particle Swarm Optimizer with a Multi-Objective Selection Scheme to Design Combinational Logic Circuits Automatic Control System Design via a Multiobjective Evolutionary Algorithm Evolutionary Multi-Objective Optimization of Trusses A Multi-Objective Evolutionary Algorithm for the Covering Tour Problem Multiobjective Aerodynamic Design and Visualization of Supersonic Wings by Using Adaptive Range Multiobjective Genetic Algorithms Mutli-Objective Spectroscopic Data Analysis of Inertial Confinement Fusion Implosion Cores: Plasma Gradient Determination On Machine Learning with Multiobjective Genetic Optimization and other papers.

Power System Optimization

Abstract: Electric power systems have experienced continuous growth in all the three major sectors of the power system namely, generation, transmission and distribution. Electricity cannot be stored economically, but there has to be continuous balance between demand and supply. The increase in load sizes and operational complexity such as generation allocation, non-utility generation planning, and pricing brought about by the widespread interconnection of transmission systems and inter-utility power transaction contracts, has introduced major difficulties into the operation of power system. Allocation of customers' load demands among the available thermal power generating units in an economic, secure and reliable way has been a subject of interest since 1920 or even earlier. However practically, the generating units have non-convex input-output characteristics due to prohibited operating zones, valve-point loadings and multi-fuel effects considered as heavy equality and inequality constraints, which cannot be directly solved by mathematical programming methods. Dynamic programming can treat such types of problems, but it suffers from the curse of dimensionality. Over the past decade, many prominent methods have been developed to solve these problems, such as the hierarchical numerical methods, tabu search, neural network approaches, genetic algorithm, evolutionary programming, swarm optimisation, differential evolution and hybrid search methods. Review of evolutionary method has been presented.

Optimal reconfiguration of radial distribution systems to maximize loadability

Abstract: This paper presents a new method for optimal reconfiguration of radial distribution systems (RDS). Optimal reconfiguration involves selection of the best set of branches to be opened, one each from each loop, such that the resulting RDS has the desired performance. Amongst the several performance criteria considered for optimal network reconfiguration, maximizing loadability is an important one. Owing to the discrete nature of the solution space, a fuzzy adaptation of the evolutionary programming algorithm for optimal reconfiguration of RDS to maximize loadability is proposed in this paper. This method maximizes a fuzzy index developed using a maximum loadability index. A 33-bus RDS is optimally reconfigured by the proposed method and the results are presented.

A study of drift analysis for estimating computation time of evolutionary algorithms

Abstract: This paper introduces drift analysis and its applications in estimating average computation time of evolutionary algorithms. Firstly, drift conditions for estimating upper and lower bounds of the mean first hitting times of evolutionary algorithms are presented. Then drift analysis is applied to two specific evolutionary algorithms and problems. Finally, a general classification of easy and hard problems for evolutionary algorithms is given based on the analysis.

Designing Evolutionary Algorithms for Dynamic Environments

Abstract: Detailsrobustness, stability, and performance of Evolutionary Algorithms in dynamic environments

Ant colony optimization and local search for bin packing and cutting stock problems

Abstract: The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO.

A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique

Abstract: This paper proposes an improved evolutionary programming (IEP) and its hybrid version combined with the nonlinear interior point (IP) technique to solve the optimal reactive power dispatch (ORPD) problems. In an IEP method, the common practices in regulating reactive power are followed in adjusting the mutation direction of control variables in order to increase the possibility of keeping state variables within bounds. The IEP is also hybridized with the IP method to obtain a fast initial solution, which is then used as a highly evolved individual in the initial population of the improved EP method. Numerical tests of the proposed algorithm on the IEEE 118-bus system and a realistic power system in Western China are very encouraging compared with the existing ORPD algorithms in term of computational efficiency and optimality.

Efficient evolutionary optimization through the use of a cultural algorithm

Abstract: This paper introduces a cultural algorithm that uses domain knowledge to improve the performance of an evolutionary programming technique adopted for constrained optimization. The proposed approach extracts domain knowledge during the evolutionary process and builds a map of the feasible region to guide the search more efficiently. Additionally, in order to have a more efficient memory management scheme, the current implementation uses 2 n -trees to store this map of the feasible region. Results indicate that the approach is able to produce very competitive results with respect to other optimization techniques at a considerably lower computational cost.

An evolutionary programming-based tabu search method for solving the unit commitment problem

Abstract: This paper presents a new approach to solving the short-term unit commitment problem using an evolutionary programming-based tabu search (TS) method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Evolutionary programming, which happens to be a global optimization technique for solving unit commitment problem, operates on a system, which is designed to encode each unit's operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all of the units according to their initial status ("flat start"). Here, the parents are obtained from a predefined set of solutions (i.e., each and every solution is adjusted to meet the requirements). Then, a random decommitment is carried out with respect to the unit's minimum downtimes, and TS improves the status by avoiding entrapment in local minima. The best population is selected by evolutionary strategy. The Neyveli Thermal Power Station (NTPS) Unit-II in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different power systems consisting of 10, 26, and 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like dynamic programming, Lagrangian relaxation, and simulated annealing and tabu search in reaching proper unit commitment.

Optimal coordinated voltage control for power system voltage stability

Abstract: An optimal coordinated voltage controller (OCVC) is developed based on the spirit of model predictive control (MPC) method. The OCVC consists of three components, namely a predictor, a control candidate pool, and a selector. It has been used in secondary voltage control (SVC) to coordinate dissimilar control actions at different geographical locations in order to maintain desired voltage profiles in a global sense in emergencies. A single-stage Euler state predictor (SESP) is utilized, based on the system model, to predict voltage performance under selected control actions; the selection of the optimum control action from the pool is a complex optimization problem that is achieved by a pseudogradient evolutionary programming (PGEP) technique. Simulation results on a six-bus benchmark system and the New England 10-generator-39-bus system are given to show the potential of this method for online usage.

An evolutionary algorithm approach to poetry generation

Abstract: Poetry is a unique artifact of the human language faculty, with its defining feature being a strong unity between content and form. Contrary to the opinion that the automatic generation of poetry is a relatively easy task, we argue that it is in fact an extremely difficult task that requires intelligence, world and linguistic knowledge, and creativity. We propose a model of poetry generation as a state space search problem, where a goal state is a text that satisfies the three properties of meaningfulness, grammaticality, and poeticness. We argue that almost all existing work on poetry generation only properly addresses a subset of these properties. In designing a computational approach for solving this problem, we draw upon the wealth of work in natural language generation (NLG). Although the emphasis of NLG research is on the generation of informative texts, recent work has highlighted the need for more flexible models which can be cast as one end of a spectrum of search sophistication, where the opposing end is the deterministically goal-directed planning of traditional NLG. We propose satisfying the properties of poetry through the application to NLG of evolutionary algorithms (EAs), a wellstudied heuristic search method. MCGONAGALL is our implemented instance of this approach. We use a linguistic representation based on Lexicalized Tree Adjoining Grammar (LTAG) that we argue is appropriate for EA-based NLG. Several genetic operators are implemented, ranging from baseline operators based on LTAG syntactic operations to heuristic semantic goal-directed operators. Two evaluation functions are implemented: one that measures the isomorphism between a solution’s stress pattern and a target metre using the edit distance algorithm, and one that measures the isomorphism between a solution’s propositional semantics and a target semantics using structural similarity metrics. We conducted an empirical study using MCGONAGALL to test the validity of employing EAs in solving the search problem, and to test whether our evaluation functions adequately capture the notions of semantic and metrical faithfulness. We conclude that our use of EAs offers an innovative approach to flexible NLG, as demonstrated by its successful application to the poetry generation task.

Evolutionary Algorithms for Multi-Criterion Optimization: A Survey

Abstract: In this paper, we review some of the most popular evolutionary algorithms and a systematic comparison among them. Then we show its importance in single and multi-objective optimization problems. Thereafter focuses some of the multi-objective evolutionary algorithms, which are currently being used by many researchers, and merits & demerits of multi-objective evolutionary algorithms (MOEAs). Finally, the future trends in this area and some possible paths of further research are addressed.

Survey on multiobjective evolutionary and real coded genetic algorithms

Abstract: Evolutionary Algorithm (EA) possesses several characteristics that are desirable to solve real-world optimization problems up to a required level of satisfaction. Multiobjective Evolutionary Algorithms (MOEAs) are designed with regard to two common goals, fast and reliable convergence to the Pareto set and a good distribution of solutions along the front. Virtually each algorithm represents a unique combination of specific techniques to achieve these goals. Handling continuous search space with binary coded genetic algorithm has several difficulties. Real coded genetic algorithm represents parameters without coding, which makes representation of the solutions very close to the natural formulation of many problems. In real coded GA (RCGA) recombination and mutation operators are designed to work with real parameters. This survey gives state-of‐the-art of multiobjective evolutionary algorithms and real coded genetic algorithms.

Building Better Nurse Scheduling Algorithms

Abstract: The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification.

Evolvable Components: From Theory to Hardware Implementations

Abstract: 1 Introduction.- 1.1 Natural Computing.- 1.1.1 Soft Computing.- 1.1.2 Quantum Computing.- 1.1.3 DNA Computing.- 1.1.4 Membrane Computing.- 1.2 Bioinspired Hardware.- 1.3 Motivation for Research.- 2 Reconfigurable Hardware.- 2.1 Digital Cicuits.- 2.2 Digital Circuit Design.- 2.3 Field Programmable Gate arrays.- 2.3.1 Architecture of FPGAs.- 2.3.2 The XC4000 Family.- 2.3.3 ThE Virtex Family.- 2.3.4 The XC6200 Family.- 2.3.5 Atmel FPGAs.- 2.3.6 Features of FPGAs.- 2.4 Hardware Reused as Software.- 2.5 Reconfigurable Computing.- 2.6 Nanotechnology.- 2.7 Cell Matrix.- 2.8 Summary.- 3 Evolutionary Algorithms.- 3.1 Introduction.- 3.2 Variant of Evolutionary Algorithms.- 3.2.1 Genetic Algorithms.- 3.2.2 Genetic Programming.- 3.2.3 Evolutionary Strategies.- 3.2.4 Evolutionary Programming.- 3.3 Some Other Features of Evolutionary Algorithms.- 3.3.1 Parallel Implementations.- 3.3.2 Dynamic Fitness Function.- 3.4 Evolutionary Design and Optimization.- 3.5 The Evolutionary Algorithm Design.- 3.5.1 Missing Theories.- 3.5.2 The Design Strategies.- 3.6 Formal Approach.- 3.7 Summary.- 4 Evolvable Hardware.- 4.1 Basic Concept.- 4.2 Cartesian Genetic Programming.- 4.3 Features of Cartesian Genetic Programming.- 4.3.1 Redundancy and Neutrality.- 4.3.2 Fitness Landscape Analysis.- 4.3.3 Implementation Issues.- 4.4 From Chromosome to Fitness Value.- 4.4.1 Representation.- 4.4.2 Platforms for Circuit Evolution.- 4.4.3 Circuit Evaluation.- 4.5 Fitness Function.- 4.5.1 Fitness Function and Circuit Behavior.- 4.5.2 Evolutionary Circuit Design: Static Fitness Function.- 4.5.3 Evolvable Hardware: Dynamic Fitness Function.- 4.5.4 Discussion.- 4.6 Applications and Degree of Hardware Implementation.- 4.7 Promising Results.- 4.8 Major Current Problems and Potential Solutions.- 4.8.1 Scalability of Representaion.- 4.8.2 SCalability of Fitnes Evaluation.- 4.8.3 Robustness of the Evolved Circuits.- 4.8.4 Applications in Dynamic Environments.- 4.9 Summary.- 5 Towards Evolvable Components.- 5.1 Component Approach to Problem Solving.- 5.2 Evolvable Components.- 5.2.1 System Decomposition.- 5.2.2 Interface.- 5.3 Hardware Implementation.- 5.3.1 Evolvable Componenets.- 5.3.2 Environment.- 5.3.3 Communication Betweem Evolvable Component and Environment.- 5.4 Extension of Evolvable Components.- 5.5 Summary.- 6 Evolvable Computational Machines.- 6.1 Computational Machines and Evolutionary Design.- 6.2 Cellular Automata.- 6.2.1 Basic Model.- 6.2.2 Evolvable Non-Uniform CEllular Automaton.- 6.2.3 An example: Evolvable Non-Uniform Cellular Automaton as a Sequence Generator.- 6.3 General Evolvable Computational Machine.- 6.4 Dynamic Environment.- 6.5 Evolvable Computational System.- 6.5.1 Formal Definition.- 6.5.2 An example: Formal Description of a Simple Image Compression.- 6.6 Properties of Evolvable Machines.- 6.6.1 On the Computation of Evolvable Machines.- 6.6.2 Mappings g and f.- 6.6.3 Changing Fitness Fuction.- 6.7 The Computational Power.- 6.7.1 The Turing Machine and the Church Turing Thesis.- 6.7.2 Beyond the Turing Machines.- 6.7.3 A New Paradigm.- 6.7.4 Site Machine.- 6.7.5 the Power of an Evolvable System.- 6.7.6 Discussion.- 6.8 Summary.- 7 An Evolvable Component for Image Pre-processing.- 7.1 Motivation and Problem Specification.- 7.2 The Image Filter Design.- 7.2.1 Types of Noise Considered for Testing.- 7.2.2 Convnetional Approaches.- 7.2.3 Implementation of FPGAs.- 7.2.4 A Brief Survey of Evolutionary Approaches.- 7.3 Analysis of Reconfigurability and Size of the Search Space.- 7.3.1 Elementary Measures.- 7.3.2 Cartesian Genetic Programming in Hardware.- 7.3.3 Cartesian Genetic Programming at the Fuctional Level.- 7.4 Evolutionary Design: Experimental Framework.- 7.4.1 Reconfigurable Circuit.- 7.4.2 Evolutionary Algorithms.- 7.4.3 Fitness Function.- 7.5 Filters for Smoothing.- 7.5.1 The Results.- 7.5.2 Discussion.- 7.6 Other Image Operators.- 7.6.1 "Salt and Pepper" Noise Filters.- 7.6.2 Random Shot-Noise Filters.- 7.6.3 Edge Detectors.- 7.7 Dynamics Environment.- 7.7.1 Experimental Setup.- 7.7.2 The Results in Tables 7.9 and 7.10.- 7.7.3 Discussion.- 7.8 A Note on a Single Filter Design.- 7.9 Summary.- Virtual Reconfigurable Devices.- 8.1 Chip on Top of a Chip.- 8.2 Architecture of Virtual Reconfigurable Circuits.- 8.2.1 Overview.- 8.2.2 Routing Logic and Configuration Memory.- 8.2.3 Configuration Options.- 8.3 Implementation Costs.- 8.4 Speeding up the Evolutionary Design.- 8.5 Genetic Unit.- 8.6 Physical Realization.- 8.7 Discussion.- 8.8 Summary.- 9 Concluding Statements.- 9.1 The Approach.- 9.2 The Obtained Results.- 9.3 Future Work.- References.

An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach

Abstract: Given the explosive growth of data collected from current business environment, data mining can potentially discover new knowledge to improve managerial decision making. This paper proposes a novel data mining approach that employs an evolutionary algorithm to discover knowledge represented in Bayesian networks. The approach is applied successfully to handle the business problem of finding response models from direct marketing data. Learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a novel hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian network models are generated by using an evolutionary algorithm. A new operator is introduced to further enhance the search effectiveness and efficiency. In a number of experiments and comparisons, the hybrid algorithm outperforms MDLEP, our previous algorithm which uses evolutionary programming (EP) for network learning, and other network learning algorithms. We then apply the approach to two data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with those by MDLEP, the logistic regression models, the na/spl inodot//spl uml/ve Bayesian classifiers, and the tree-augmented na/spl inodot//spl uml/ve Bayesian network classifiers (TAN). In the comparison, the new algorithm outperforms the others.

Transonic axial-flow blade optimization: Evolutionary algorithms/three- dimensional Navier-Stokes solver

Abstract: The development of a high-fidelity aerodynamic design optimization tool based on evolutionary algorithms for turbomachinery is attempted. A three-dimensional Navier-Stokes solver was used for aerodynamic analysis, so thatflowfields would be represented accurately and so that realistic and reliable designs would be produced. For efficient and robust design optimization, the real-coded adaptive range genetic algorithm was adopted, and the computation was parallelized and performed on an SGI Origin 2000 cluster to reduce turnaround time. The aerodynamic redesign of the NASA rotor 67 blade demonstrated the superiority of the present method over the conventional design approach, increasing adiabatic efficiency by 2% over the original design. This increase is achieved not only at the design condition, but over the entire operating range. This design optimization method has proven to be suitable for parallel computing. This promising tool is shown to help turbomachinery designers to design higher-performance machines while shortening the design cycle and reducing design costs.

Evaluating a hybrid encoding and three crossover operators on the constrained portfolio selection problem

Abstract: In this paper we investigate the impact of different crossover operators for a real-valued evolutionary algorithm on the constrained portfolio selection problem based on the Markowitz mean-variance model. We also introduce an extension of a real-valued genotype, which increases the performance of the evolutionary algorithm significantly, independent of the crossover operator used. This extension is based on the effect that most efficient portfolios only consist of a selection of few assets. Therefore, the portfolio selection problem is actually a combination of a knapsack and continuous parameter problem. We also introduce a repair mechanism and examine the impact of Lamarckism on the performance of the evolutionary algorithm.

Optimized Design of MEMS by Evolutionary Multi-objective Optimization with Interactive Evolutionary Computation

Abstract: We combine interactive evolutionary computation (IEC) with existing evolutionary synthesis software for the design of micromachined resonators and evaluate its effectiveness using human evaluation of the final designs and a test for statistical significance of the improvements. The addition of IEC produces superior designs with fewer potential design or manufacturing problems than those produced through the evolutionary synthesis software alone as it takes advantage of the human ability to perceive design flaws that cannot currently be simulated. A user study has been performed to compare the effectiveness of the IEC enhanced software with the non-interactive software. The results show that the IEC-enhanced synthesis software creates a statistically significant greater number of designs rated best by users.

GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms

Abstract: Evolutionary techniques are one of the most successful paradigms in the field of optimization. In this paper we present a new approach, named GA-EDA, which is a new hybrid algorithm based on genetic and estimation of distribution algorithms. The original objective is to get benefits from both approaches. In order to perform an evaluation of this new approach a selection of synthetic optimizations problems have been proposed together with two real-world cases. Experimental results show the correctness of our new approach.

Parallel evolutionary algorithms for uav path planning

Abstract: Evolutionary computation (EC) techniques have been successfully applied to compute near-optimal paths for unmanned aerial vehicles (UAVs). Premature convergence prevents evolutionary-based algorithms from reaching global optimal solutions. This often leads to unsatisfactory routes that are suboptimal to optimal path planning problems. To overcome this problem, this paper presents a framework of parallel evolutionary algorithms for UAV path planning, in which several populations evolve simultaneously and compete with each other. The parallel evolution technique provides more exploration capability to planners and significantly reduces the probability that planners are trapped in local optimal solutions.

Fitness and evolutionary stability in game theoretic models of finite populations.

Abstract: We investigate two methods of measuring fitness in evolutionary games played among members of a finite population. Classical notions of stability account for the action of selection only, and use immediate reproductive gains as a measure of fitness. This classical interpretation of fitness is what we call reproductive fitness (RF), and is found in the early studies of evolutionary stability in finite populations. More recent work has incorporated the influence of random genetic drift by applying fixation probability (FP) as a measure of fitness. When defined in this way, fitness represents a measure of ultimate evolutionary success. Our main result describes an equivalence between candidate evolutionarily stable strategies under both the RF and FP interpretations of fitness. We apply this result to matrix games in which the use of mixed strategies is permitted, and find here an equivalence between the RF and FP conditions for evolutionary stability.