Download or read book Evolutionary Constrained Optimization written by Rituparna Datta and published by Springer. This book was released on 2014-12-13 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.

Download or read book Evolutionary Computations written by Keigo Watanabe and published by Springer. This book was released on 2012-11-02 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary computation, a broad field that includes genetic algorithms, evolution strategies, and evolutionary programming, has proven to offer well-suited techniques for industrial and management tasks - therefore receiving considerable attention from scientists and engineers during the last decade. This monograph develops and analyzes evolutionary algorithms that can be successfully applied to real-world problems such as robotic control. Although of particular interest to robotic control engineers, Evolutionary Computations also may interest the large audience of researchers, engineers, designers and graduate students confronted with complicated optimization tasks.

Download or read book Constraint Handling in Evolutionary Optimization written by Efrén Mezura-Montes and published by Springer Science & Business Media. This book was released on 2009-04-07 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the result of a special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007. It presents recent research in constraint-handling in evolutionary optimization.

Download or read book Evolutionary Approach for Constrained Optimization written by Saber Elsayed and published by . This book was released on 2012 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: Constrained optimization is a challenging area of research in the science and engineering disciplines. Locating the optimal solutions for such problems is often difficult as their characteristics and mathematical properties do not follow any standard patterns or forms. Over the last few decades, evolutionary algorithms (EAs) have been widely used for solving optimization problems. Although there are many EAs for solving Constrained Optimization Problems (COPs), no single algorithm performs consistently over a wide range of problems. Therefore, ideas for multi-operator- and multi-methodology-based algorithms have recently been introduced but their actual strengths for solving COPs have not been fully explored. The choice of operators and/or algorithms and their appropriate mix, and a strategy for their use in designing an effective EA have not been well studied. In this thesis, a general framework for solving COPs that allows the use of different operators and/or algorithms under a single algorithmic structure is proposed and a good number of algorithms are designed, implemented, analyzed and tested. In this research, firstly, multi-operator EAs are studied. They can be implemented in many different ways, such as with and without population divisions, adaptations and local searches, and offer different ways of choosing and mixing operators. To test the robustness of this concept, two different EAs, known as the genetic algorithm (GA) and differential evolution (DE), are considered. Different variants of a multi-operator GA and multioperator DE are implemented with and without self-adaptation, population changes and local searches. The purpose of introducing self-adaptation is for the appropriate mix of operators to be automatically chosen with the progress of evolution. To do this, a new self-adaptive mechanism is derived and its number and choice of operators are discussed and justified. Initially, one type of operator, a crossover for GA and a mutation for DE, is considered. Later, these algorithms are extended to include multiple crossover and mutation operators and designed to use a self-adaptive mechanism to change the number of individuals assigned to each. The DE-based algorithm is further extended to include different constraint-handling techniques (CHTs). In its implementation, each chromosome contains information on the use of a single crossover, a single mutation and a single CHT. To accelerate the performances of these algorithms, a local search technique is also applied. The framework proposed for an EA with multiple operators is further extended to include multiple EAs under a single algorithmic framework. Therefore, a new algorithm, in which the population is divided into sub-populations, where each subpopulation runs a defined multi-operator EA, is introduced. The number of EAs used are discussed and analyzed. To demonstrate consistent performances of all the proposed algorithms, they solve the problems from two different specialized sets of benchmark problems and a detailed parametric analysis of them is provided. For comparisons of the competing algorithms, a new comparison matrix is introduced. The results from this thesis can be summarized as follows: (1) the multi-operator based GA is able to obtain much better solutions than those of each independent GA with a single operator; (2) the use of the self-adaptive mechanism leads to much better solutions and savings in time; (3) the multi-operator based GA is competitive with the state-of-the-art algorithms; (4) the self-adaptive multioperator-based DE is not only better than the single operator-based DE but also takes 25% less computational time than a standard DE; (5) the self-adaptive multi-operatorbased DE with a local search technique performs much better and takes 48% less computational than the algorithm without a local search; (6) the self-adaptive DE with a mix of crossover and mutation operators plus a local search is better than those without a local search and is also superior to the state-of-the-art algorithms; (7) DE with multiple crossover and mutation operators plus CHTs is able to obtain better solutions than DE with a single operator and different state-of-the-art algorithms; (8) the multimethodology EA with three different algorithms performs best, is superior to DE with a multiple mutation operators the state-of-the-art algorithms and consumes 26% less computational time than the self-adaptive multi-operator DE; and (9) considering all the algorithms implemented and tested in this thesis, the memetic self-adaptive multioperator DE is the best.

Download or read book Constraint Handling in Evolutionary Optimization written by Efrén Mezura-Montes and published by Springer. This book was released on 2009-05-03 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the result of a special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007. It presents recent research in constraint-handling in evolutionary optimization.

Download or read book Evolutionary Optimization written by Ruhul Sarker and published by Springer Science & Business Media. This book was released on 2006-04-11 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.

Download or read book Differential Evolution written by Kenneth Price and published by Springer Science & Business Media. This book was released on 2006-03-04 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.

Download or read book Self Organizing Migrating Algorithm written by Donald Davendra and published by Springer. This book was released on 2016-02-04 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.

Download or read book Advances in Artificial Intelligence IBERAMIA 2004 written by Christian Lemaitre and published by Springer. This book was released on 2004-11-03 with total page 1005 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2004, held in Puebla, Mexico in November 2004. The 97 revised full papers presented were carefully reviewed and selected from 304 submissions. The papers are organized in topical sections on distributed AI and multi-agent systems, knowledge engineering and case-based reasoning, planning and scheduling, machine learning and knowledge acquisition, natural language processing, knowledge representation and reasoning, knowledge discovery and data mining, robotics, computer vision, uncertainty and fuzzy systems, genetic algorithms and neural networks, AI in education, and miscellaneous topics.

Download or read book Reliability Based Optimization f r Multiple Constraints with Evolutionary Algorithms written by David Daum and published by diplom.de. This book was released on 2014-04-11 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inhaltsangabe:Introduction: In handling real-world optimization problems, it is often the case that the underlying decision variables and parameters cannot be controlled exactly as specified. For example, if a deterministic consideration of an optimization problem results in an optimal dimension of a cylindrical member to have a 50 mm diameter, there exists no manufacturing process which will guarantee the production of a cylinder having exactly a 50 mm diameter. Every manufacturing process has a finite machine precision and the dimensions are expected to vary around the specified value. Similarly, the strength of a material often does not remain fixed for the entire length of the material and is expected to vary from point to point. When such variations in decision variables and parameters are expected in practice, an obvious question arises: How reliable is the optimized design against failure when the suggested parameters cannot be adhered to? This question is important because in most optimization problems the deterministic optimum lies at the intersection of a number of constraint boundaries. Thus, if no uncertainties in parameters and variables are expected, the optimized solution is the best choice, but if uncertainties are expected, in most occasions, the optimized solution will be found to be infeasible, violating one or more constraints. These uncertainties, which are either controllable (e.g.imensions) or uncontrollable (e.g. material properties), are present and need to be accounted for in the design process. Assuming that the variables follow a probability distribution in practice, reliability-based design optimization (RBDO) methods find a reliable solution which is feasible with a pre-specified probability. In most RBDO problems, failure probability and costs are violating objectives, which means that when one is lowered, the other may rise. Therefore, it is important to identify the uncertain variables which have an impact on the problem and describe them with different probability distributions based on statistical calculations. Then, the ordinary deterministic constraint is replaced by a stochastic constraint which is only restricting the probability of failure for a solution, not the failure itself. This can be done for each constraint or for the complete set of constraints, for the complete structure. Different methods for evaluating the reliability of a solution exist. If the cumulative density function (CDF) with its [...]

Download or read book Evolutionary Algorithms for Constrained Optimization Problems written by Jens Gottlieb and published by . This book was released on 1999 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Evolutionary Multiobjective Optimization written by Ajith Abraham and published by Springer Science & Business Media. This book was released on 2005-09-05 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field. The topic and content is currently very fashionable and has immense potential for practical applications and includes contributions from leading researchers in the field. Assembled in a compelling and well-organised fashion, Evolutionary Computation Based Multi-Criteria Optimization will prove beneficial for both academic and industrial scientists and engineers engaged in research and development and application of evolutionary algorithm based MCO. Packed with must-find information, this book is the first to comprehensively and clearly address the issue of evolutionary computation based MCO, and is an essential read for any researcher or practitioner of the technique.

Download or read book A Brief Introduction to Continuous Evolutionary Optimization written by Oliver Kramer and published by Springer Science & Business Media. This book was released on 2013-12-04 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.

Download or read book Parallel Problem Solving from Nature PPSN III written by Yuval Davidor and published by . This book was released on 1994 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This volume comprises the 61 revised refereed papers accepted for presentation at the ICEC/PPSN III conferences held jointly in Jerusalem, Israel in October 1994. With the appearance of more and more powerful computers, there is increased interest in algorithms relying upon analogies to natural processes. This book presents a wealth of new theoretical and experimental results on artificial problem solving by applying evolutionary computation metaphors, including evolution strategies, evolutionary programming, genetic algorithms, genetic programming, and classifier systems. Topics such as simulated annealing, immune networks, neural networks, fuzzy systems, and complex, real-world optimization problems are also treated."--Publisher's Website.

Download or read book Data Driven Evolutionary Optimization written by Yaochu Jin and published by Springer Nature. This book was released on 2021-06-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.

Download or read book Evaluation of Active Set Evolution Strategies for Optimization with Known Constraints written by Zehao Ba and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolution strategy (ES) is most often used to solve unconstrained black-box problems, while active-set methods focus on solving constrained optimization problems. A recent algorithm combines (1+1)-ES and an active-set method to get an active-set evolution strategy to solve problems in which the objective function is considered a black-box, but the constraint functions are known explicitly. We observe that the previous active-set evolution strategies have some settings result in less than optimal performance, so we make some adjustments to the past algorithms. More importantly, we systematically evaluate the performances of the two previous active-set evolution strategies with our modified version on the spherically symmetric functions with mutually orthogonal linear constraints. We also compare the performances of the modified version with three deterministic algorithms and an evolutionary algorithm. The test set we use is from the IEEE Congress on Evolutionary Computation (CEC) Competitions in 2006.

Download or read book Artificial Neural Nets and Genetic Algorithms written by Andrej Dobnikar and published by Springer Science & Business Media. This book was released on 1999-07-15 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the contents: Neural networks – theory and applications: NNs (= neural networks) classifier on continuous data domains– quantum associative memory – a new class of neuron-like discrete filters to image processing – modular NNs for improving generalisation properties – presynaptic inhibition modelling for image processing application – NN recognition system for a curvature primal sketch – NN based nonlinear temporal-spatial noise rejection system – relaxation rate for improving Hopfield network – Oja's NN and influence of the learning gain on its dynamics Genetic algorithms – theory and applications: transposition: a biological-inspired mechanism to use with GAs (= genetic algorithms) – GA for decision tree induction – optimising decision classifications using GAs – scheduling tasks with intertask communication onto multiprocessors by GAs – design of robust networks with GA – effect of degenerate coding on GAs – multiple traffic signal control using a GA – evolving musical harmonisation – niched-penalty approach for constraint handling in GAs – GA with dynamic population size – GA with dynamic niche clustering for multimodal function optimisation Soft computing and uncertainty: self-adaptation of evolutionary constructed decision trees by information spreading – evolutionary programming of near optimal NNs