Showing papers on "Evolutionary programming published in 2005"

A particle swarm optimization for economic dispatch with nonsmooth cost functions

Abstract: This work presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. A modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. A constraint treatment mechanism is devised in such a way that the dynamic process inherent in the conventional PSO is preserved. Moreover, a dynamic search-space reduction strategy is devised to accelerate the optimization process. To show its efficiency and effectiveness, the proposed MPSO is applied to test ED problems, one with smooth cost functions and others with nonsmooth cost functions considering valve-point effects and multi-fuel problems. The results of the MPSO are compared with the results of conventional numerical methods, Tabu search method, evolutionary programming approaches, genetic algorithm, and modified Hopfield neural network approaches.

Complex Adaptations and the Evolution of Evolvability

Abstract: 1 One may wonder, ...] how complex organisms evolve at all. They seem to have so many genes, so many multiple or pleiotropic eeects of any one gene, so many possibilities for lethal mutations in early development, and all sorts of problems due to their long development. Abstract: The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian process of mutation, recombination and selection is not universally eeective in improving complex systems like computer programs or chip designs. For adaptation to occur, these systems must possess "evolvability", i.e. the ability of random variations to sometimes produce improvement. It was found that evolvability critically depends on the way genetic variation maps onto phenotypic variation, an issue known as the representation problem. The genotype-phenotype map determines the variability of characters, which is the propensity to vary. Variability needs to be distinguished from variation, which are the actually realized diierences between individuals. The genotype-phenotype map is the common theme underlying such varied biological phenomena as genetic canalization, developmental constraints, biological versatility , developmental dissociability, morphological integration, and many more. For evolutionary biology the representation problem has important implications: how is it that extant species acquired a genotype-phenotype map which allows improvement by mutation and selection? Is the genotype-phenotype map able to change in evolution? What are the selective forces, if any, that shape the genotype-phenotype map? We propose that the genotype-phenotype map can evolve by two main routes: epistatic mutations, or the creation of new genes. A common result for organismic design is modularity. By modularity we mean a genotype-phenotype map in which there are few pleiotropic eeects among characters serving diierent functions, with pleiotropic eeects falling mainly among characters that are part of a single functional complex. Such a design is expected to improve evolvability by limiting the interference between the adaptation of diierent functions. Several population genetic models are reviewed that are intended to explain the evolutionary origin of a modular design. While our current knowledge is insuucient to assess the plausibil-ity of these models, they form the beginning of a framework for understanding the evolution of the genotype-phenotype map.

Evolutionary programming as a platform for in silico metabolic engineering

Abstract: Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.

Evolutionary multiobjective optimization : theoretical advances and applications

Abstract: Evolutionary Multiobjective Optimization Recent Trends in Evolutionary Multiobjective Optimization Self-adaptation and Convergence of Multiobjective Evolutionary Algorithms in Continuous Search Spaces A simple approach to evolutionary multi-objective optimization Quad-trees: A Data Structure for Storing Pareto-sets in Multi-objective Evolutionary Algorithms with Elitism Scalable Test Problems for Evolutionary Multi-Objective Optimization Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multi-Criteria Optimization Problems Evolving Continuous Pareto Regions MOGADES: Multi-Objective Genetic Algorithm with Distributed Environment Scheme Use of Multiobjective Optimization Concepts to Handle Constraints in Genetic Algorithms Multi- Criteria Optimization of Finite State Automata: Maximizing Performance while Minimizing Description Length Multi-objective Optimization of Space Structures under Static and Seismic Loading Conditions

On the Choice of the Offspring Population Size in Evolutionary Algorithms

Abstract: Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a difficult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.

Multiobjective Evolutionary Algorithms and Applications

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Application and comparison of metaheuristic techniques to generation expansion planning problem

Abstract: This work presents both application and comparison of the metaheuristic techniques to generation expansion planning (GEP) problem. The Metaheuristic techniques such as the genetic algorithm, differential evolution, evolutionary programming, evolutionary strategy, ant colony optimization, particle swarm optimization, tabu search, simulated annealing, and hybrid approach are applied to solve GEP problem. The original GEP problem is modified using the proposed methods virtual mapping procedure (VMP) and penalty factor approach (PFA), to improve the efficiency of the metaheuristic techniques. Further, intelligent initial population generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the three test systems. The GEP problem considered synthetic test systems for 6-year, 14-year, and 24-year planning horizon having five types of candidate units. The results obtained by all these proposed techniques are compared and validated against conventional dynamic programming and the effectiveness of each proposed methods has also been illustrated in detail.

A particle swarm optimization to identifying the ARMAX model for short-term load forecasting

Abstract: In this paper, a new particle swarm optimization (PSO) approach to identifying the autoregressive moving average with exogenous variable (ARMAX) model for one-day to one-week ahead hourly load forecasts was proposed Owing to the inherent nonlinear characteristics of power system loads, the surface of the forecasting error function possesses many local minimum points Solutions of the gradient search-based stochastic time series (STS) technique may, therefore, stall at the local minimum points, which lead to an inadequate model By simulating a simplified social system, the PSO algorithm offers the capability of converging toward the global minimum point of a complex error surface The proposed PSO has been tested on the different types of Taiwan Power (Taipower) load data and compared with the evolutionary programming (EP) algorithm and the traditional STS method Testing results indicate that the proposed PSO has high-quality solution, superior convergence characteristics, and shorter computation time

Mutation-based genetic neural network

Abstract: Evolving gradient-learning artificial neural networks (ANNs) using an evolutionary algorithm (EA) is a popular approach to address the local optima and design problems of ANN. The typical approach is to combine the strength of backpropagation (BP) in weight learning and EA's capability of searching the architecture space. However, the BP's "gradient descent" approach requires a highly computer-intensive operation that relatively restricts the search coverage of EA by compelling it to use a small population size. To address this problem, we utilized mutation-based genetic neural network (MGNN) to replace BP by using the mutation strategy of local adaptation of evolutionary programming (EP) to effect weight learning. The MGNN's mutation enables the network to dynamically evolve its structure and adapt its weights at the same time. Moreover, MGNN's EP-based encoding scheme allows for a flexible and less restricted formulation of the fitness function and makes fitness computation fast and efficient. This makes it feasible to use larger population sizes and allows MGNN to have a relatively wide search coverage of the architecture space. MGNN implements a stopping criterion where overfitness occurrences are monitored through "sliding-windows" to avoid premature learning and overlearning. Statistical analysis of its performance to some well-known classification problems demonstrate its good generalization capability. It also reveals that locally adapting or scheduling the strategy parameters embedded in each individual network may provide a proper balance between the local and global searching capabilities of MGNN.

Evolutionary computing in manufacturing industry: an overview of recent applications

Abstract: Traditional methods often employed to solve complex real world problems tend to inhibit elaborate exploration of the search space. They can be expensive and often results in sub-optimal solutions. Evolutionary computation (EC) is generating considerable interest for solving real world engineering problems. They are proving robust in delivering global optimal solutions and helping to resolve limitations encountered in traditional methods. EC harnesses the power of natural selection to turn computers into optimisation tools. The core methodologies of EC are genetic algorithms (GA), evolutionary programming (EP), evolution strategies (ES) and genetic programming (GP). This paper attempts to bridge the gap between theory and practice by exploring characteristics of real world problems and by surveying recent EC applications for solving real world problems in the manufacturing industry. The survey outlines the current status and trends of EC applications in manufacturing industry. For each application domain, the paper describes the general domain problem, common issues, current trends, and the improvements generated by adopting the GA strategy. The paper concludes with an outline of inhibitors to industrial applications of optimisation algorithms.

Evolving Evolutionary Algorithms Using Linear Genetic Programming

Abstract: A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

Evolutionary Scheduling: A Review

Abstract: Early and seminal work which applied evolutionary computing methods to scheduling problems from 1985 onwards laid a strong and exciting foundation for the work which has been reported over the past decade or so. A survey of the current state-of-the-art was produced in 1999 for the European Network of Excellence on Evolutionary Computing EVONET?this paper provides a more up-to-date overview of the area, reporting on current trends, achievements, and suggesting the way forward.

Memetic Evolutionary Algorithms

Abstract: Memetic Evolutionary Algorithms (MAs) are a class of stochastic heuristics for global optimization which combine the parallel global search nature of Evolutionary Algorithms with Local Search to improve individual solutions. These techniques are being applied to an increasing range of application domains with successful results, and the aim of this book is both to highlight some of these applications, and to shed light on some of the design issues and considerations necessary to a successful implementation. In this chapter we provide a background for the rest of the volume by introducing Evolutionary Algorithms (EAs) and Local Search. We then move on to describe the synergy that arises when these two are combined in Memetic Algorithms, and to discuss some of the most salient design issues for a successful implementation. We conclude by describing various other ways in which EAs and MAs can be hybridized with domain-specific knowledge and other search techniques.

Knowledge incorporation in evolutionary computation

Abstract: This carefully edited book puts together the state of the art and recent advances in knowledge incorporation in evolutionary computation within a unified ...

Decision Support for Watershed Management Using Evolutionary Algorithms

Abstract: An integrative computational methodology is developed for the management of nonpoint source pollution from watersheds. The associated decision support system is based on an interface between evolutionary algorithms (EAs) and a comprehensive watershed simulation model, and is capable of identifying optimal or near-optimal land use patterns to satisfy objectives. Specifically, a genetic algorithm (GA) is linked with the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT) for single objective evaluations, and a Strength Pareto Evolutionary Algorithm has been integrated with SWAT for multiobjective optimization. The model can be operated at a small spatial scale, such as a farm field, or on a larger watershed scale. A secondary model that also uses a GA is developed for calibration of the simulation model. Sensitivity analysis and parameterization are carried out in a preliminary step to identify model parameters that need to be calibrated. Application to a demonstration watershed located in Southern Illinois reveals the capability of the model in achieving its intended goals. However, the model is found to be computationally demanding as a direct consequence of repeated SWAT simulations during the search for favorable solutions. An artificial neural network (ANN) has been developed to mimic SWAT outputs and ultimately replace it during the search process. Replacement of SWAT by the ANN results in an 84% reduction in computational time required to identify final land use patterns. The ANN model is trained using a hybrid of evolutionary programming (EP) and the back propagation (BP) algorithms. The hybrid algorithm was found to be more effective and efficient than either EP or BP alone. Overall, this study demonstrates the powerful and multifaceted role that EAs and artificial intelligence techniques could play in solving the complex and realistic problems of environmental and water resources systems.

Gaussian particle swarm with jumps

Abstract: Gaussian particle swarm optimization (GPSO) algorithm has shown promising results for solving multimodal optimization problems in low dimensional search space. But similar to evolutionary algorithms (EAs), GPSO may also get stuck in local minima when optimizing functions with many local minima like the Rastrigin or Riewank functions in high dimensional search space. In this paper, an approach which consists of a GPSO with jumps to escape from local minima is presented. The jump strategy is implemented as a mutation operator based on the Gaussian and Cauchy probability distribution. The new algorithm was tested on a suite of well-known benchmark functions with many local optima and the results were compared with those obtained by the standard PSO algorithm, and PSO with constriction factor. Simulation results show that the GPSO with Gaussian and Cauchy jump outperforms the standard one and presents a very competitive performance compared to PSO with constriction factor and also self-adaptive evolutionary programming.

Differential Evolution, an Alternative Approach to Evolutionary Algorithm

Abstract: As a relatively new population based optimization technique, differential evolution has been attracting increasing attention for a wide variety of engineering applications including power engineering Unlike the conventional evolutionary algorithms which depend on predefined probability distribution function for mutation process, differential evolution uses the differences of randomly sampled pairs of objective vectors for its mutation process Consequently, the object vectors' differences will pass the objective functions topographical information toward the optimization process, and therefore provide more efficient global optimization capability This paper aims at providing an overview of differential evolution and presenting it as an alternative to evolutionary algorithms with two application examples in power systems

MicroGP—An Evolutionary Assembly Program Generator

Abstract: This paper describes ?GP, an evolutionary approach for generating assembly programs tuned for a specific microprocessor. The approach is based on three clearly separated blocks: an evolutionary core, an instruction library and an external evaluator. The evolutionary core conducts adaptive population-based search. The instruction library is used to map individuals to valid assembly language programs. The external evaluator simulates the assembly program, providing the necessary feedback to the evolutionary core. ?GP has some distinctive features that allow its use in specific contexts. This paper focuses on one such context: test program generation for design validation of microprocessors. Reported results show ?GP being used to validate a complex 5-stage pipelined microprocessor. Its induced test programs outperform an exhaustive functional test and an instruction randomizer, showing that engineers are able to automatically obtain high-quality test programs.

Unbiased tournament selection

Abstract: Tournament selection is a popular form of selection which is commonly used with genetic algorithms, genetic programming and evolutionary programming However, tournament selection introduces a sampling bias into the selection process We review analytic results and present empirical evidence that shows this bias has a significant impact on search performance We introduce two new forms of unbiased tournament selection that remove or reduce sampling bias in tournament selection

Real-parameter optimization using the mutation step co-evolution

Abstract: An evolutionary algorithm for the optimization of a function with real parameters is described in this paper. It uses a cooperative co-evolution to breed and reproduce successful mutation steps. The algorithm described herein is then tested on a suite of 10D and 30D reference optimization problems collected for the special session on real-parameter optimization of the IEEE Congress on Evolutionary Computation 2005. The results are of mixed quality (as expected), but reveal several interesting aspects of this simple algorithm

Evolutionary computation in data mining

Abstract: Evolutionary Algorithms for Data Mining and Knowledge Discovery.- Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining.- GAP: Constructing and Selecting Features with Evolutionary Computing.- Multi-Agent Data Mining using Evolutionary Computing.- A Rule Extraction System with Class-Dependent Features.- Knowledge Discovery in Data Mining via an Evolutionary Algorithm.- Diversity and Neuro-Ensemble.- Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets.- Evolutionary Computation in Intelligent Network Management.- Genetic Programming in Data Mining for Drug Discovery.- Microarray Data Mining with Evolutionary Computation.- An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts.

Evolutionary Algorithms with Particle Swarm Movements

Abstract: This text introduces a family of evolutionary algorithms named EPSO $evolutionary particle swarm optimization. EPSO algorithms are evolutionary methods that borrow the movement rule from particle swarm optimization methods (PSO) and use it as a recombination operator that evolves under the pressure of selection. This hybrid approach builds up an algorithm that, in several cases, in application to complex problems in power systems, has already proven to be more efficient, accurate and robust than classical evolutionary methods or classical PSO. The text presents the description of the method, didactic examples and examples of applications in real world problems

Toward a theory of evolutionary computation.

Abstract: We outline a theory of evolutionary computation using a formal model of evolutionary computation – the Evolutionary Turing Machine – which is introduced as the extension of the Turing Machine model. Evolutionary Turing Machines provide a better and a more complete model for evolutionary computing than conventional Turing Machines, algorithms, and Markov chains. The convergence and convergence rate are defined and investigated in terms of this new model. The sufficient conditions needed for the completeness and optimality of evolutionary search are investigated. In particular, the notion of the total optimality as an instance of the multiobjective optimization of the Universal Evolutionary Turing Machine is introduced. This provides an automatic way to deal with the intractability of evolutionary search by optimizing the quality of solutions and search costs simultaneously. Based on a new model a very flexible classification of optimization problem hardness for the evolutionary techniques is proposed. The expressiveness of evolutionary computation is investigated. We show that the problem of the best evolutionary algorithm is undecidable independently of whether the fitness function is time dependent or fixed. It is demonstrated that the evolutionary computation paradigm is more expressive than Turing Machines, and thus the conventional computer science based on them. We show that an Evolutionary Turing Machine is able to solve nonalgorithmically the halting problem of the Universal Turing Machine and, asymptotically, the best evolutionary algorithm problem. In other words, the best evolutionary algorithm does not exist, but it can be potentially indefinitely approximated using evolutionary techniques.