الصفحة 4
الصفحة 4
Experimental Research in Evolutionary Computation : The New Experimentalism
This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. The book develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. Treating optimization runs as experiments, the author offers methods for solving complex real-world problems that involve optimization via simulation, and he describes successful applications in engineering and industrial control projects.
Experimental Algorithms ; 7th International Workshop, WEA 2008 Provincetown, MA, USA, May 30-June 1, 2008 Proceedings
The Workshop on Experimental Algorithms, WEA, is intended to be an international forum for research on the experimental evaluation and engineering of algorithms, as well as in various aspects of computational optimization and its applications. The emphasis of the workshop is the use of experimental me- ods to guide the design, analysis, implementation, and evaluation of algorithms, heuristics, and optimization programs. WEA 2008 was held at the Provincetown Inn, Provincetown, MA, USA, on May 30 – June 1, 2008. This was the seventh workshop of the series.
Experimental Algorithms ; 5th International Workshop, WEA 2006, Cala Galdana, Menorca, Spain, May 24-27, 2006, Proceedings
This book constitutes the refereed proceedings of the 5th International Workshop on Experimental and Efficient Algorithms, WEA 2006, held in Cala Galdana, Menorca, Spain in May 2006. The 26 revised full papers presented together with 3 invited talks were carefully reviewed and selected from 92 submissions. The book is devoted to the design, analysis, implementation, experimental evaluation, and engineering of efficient algorithms. Among the application areas addressed are most fields applying advanced algorithmic techniques.
Evolutionary Scheduling
Evolutionary scheduling is a vital research domain at the interface of two important sciences - artificial intelligence and operational research. Scheduling problems are generally complex, large scale, constrained, and multi-objective in nature, and classical operational research techniques are often inadequate at solving them effectively. With the advent of computation intelligence, there is renewed interest in solving scheduling problems using evolutionary computational techniques. These techniques, which include genetic algorithms, genetic programming, evolutionary strategies, memetic algorithms, particle swarm optimization, ant colony systems, etc, are derived from biologically inspired concepts and are well-suited to solve scheduling problems since they are highly scalable and flexible in terms of handling constraints and multiple objectives. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling, and demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.
Evolutionary Multi-Criterion Optimization ; 4th International Conference, EMO 2007, Matsushima, Japan, March 5-8, 2007, Proceedings
Multicriterion optimization refers to problems with two or more objectives (normally in conflict with each other) which must be simultaneously satisfied. Evolutionary algorithms have been used for solving multicriterion optimization problems for over two decades, gaining an increasing attention from industry. This book included four keynote speakers: Hirotaka Nakayama on aspiration level methods, Kay Chen Tan on large and computationally intensive real-world MO optimization problems, Carlos Fonseca on decision making, and Gary B. Lamont on design of large-scale network centric systems.
Evolutionary Multi-Criterion Optimization ; 3rd International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005, Proceedings
Constitutes the refereed proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization, EMO 2005, held in Guanajuato, Mexico, in March 2005.
Evolutionary computation in combinatorial optimization ; Vol. 3448 ; 5th European Conference, EvoCOP 2005, Lausanne, Switzerland, March 30 - April 1, 2005, Proceedings
This volume contains the proceedings of EvoCOP 2005, the 5th European Conference on Evolutionary Computation in Combinatorial Optimization. It was held in Lausanne, Switzerland, on 30 March-1 April 2005
Evolutionary computation in combinatorial optimization ; 8th European Conference, EvoCOP 2008, Naples, Italy, March 26-28, 2008. Proceedings
Metaheuristics have been shown to be e?ective for di?cult combinatorial - timization problems appearing in various industrial, economical, and scientifc domains. Prominent examples of metaheuristics are evolutionary algorithms, tabu search, simulated annealing, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, ant colony optimization and estimation of distribution algorithms. Problems solved successfully include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman pr- lem, packing and cutting, satisfability and general mixed integer programming.
Evolutionary computation in combinatorial optimization ; 7th European Conference, EvoCOP 2007, Valencia, Spain, April 11-13, 2007, Proceedings
This book cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, greedy randomized adaptive search procedures, ant colony optimization, and particle swarm optimization algorithms. The papers are specifically dedicat.
Evolutionary Algorithms for Solving Multi-Objective Problems
This book for Solving Multi-Objective Problems, significantly augmented with contemporary knowledge and adapted for the classroom. All the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and student-friendly fashion, incorporating state-of-the-art research results. The diversity of serial and parallel MOEA structures are given, evaluated and compared. The book provides detailed insight into the application of MOEA techniques to an array of practical problems. The assortment of test suites are discussed along with the variety of appropriate metrics and relevant statistical performance techniques.
Enumerative Invariants in Algebraic Geometry and String Theory : Lectures given at the C.I.M.E. Summer School held in Cetraro, Italy June 6–11, 2005
Starting in the middle of the 80s, there has been a growing and fruitful interaction between algebraic geometry and certain areas of theoretical high-energy physics, especially the various versions of string theory. Physical heuristics have provided inspiration for new mathematical definitions (such as that of Gromov-Witten invariants) leading in turn to the solution of problems in enumerative geometry. Conversely, the availability of mathematically rigorous definitions and theorems has benefited the physics research by providing the required evidence in fields where experimental testing seems problematic. The aim of this volume, a result of the CIME Summer School held in Cetraro, Italy, in 2005, is to cover part of the most recent and interesting findings in this subject.
Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics ; International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007, Proceedings
Stochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computer science, operations research, and engineering. However, in recent years it has become evident that at the core of this development task there is a highly complex engineering process, which combines various aspects of algorithm design with empirical analysis techniques and problem-specific background, and which relies heavily on knowledge from a number of disciplines and areas, including computer science, operations research, artificial intelligence, and statistics. This development process needs to be - sisted by a sound methodology that addresses the issues arising in the various phases of algorithm design, implementation, tuning, and experimental eval- tion.
Distributed Search by Constrained Agents : Algorithms, Performance, Communication
This book presents a comprehensive discussion on the field of distributed constraints, its algorithms and its active research areas. It introduces distributed constraint satisfaction and optimization problems and proceeds to present a complete state-of-the-art in DisCSP & DisCOP research.
Differential Evolution ; Vol.5 : In Search of Solutions
The human being aspires to the best possible performance. Both individuals and enterprises are looking for optimal—in other words, the best possible—solutions for situations or problems they face. Most of these problems can be expressed in mathematical terms, and so the methods of optimization undoubtedly render a significant aid. In cases where there are many local optima; intricate constraints; mixed-type variables; or noisy, time-dependent or otherwise ill-defined functions, the usual methods don’t give satisfactory results. Are you seeking fresh ideas or more efficient methods, or do you perhaps want to be well-informed about the latest achievements in optimization? If so, this book is for you. This book develops a unified insight on population-based optimization through Differential Evolution, one of the most recent and efficient optimization algorithms. You will find, in this book, everything concerning Differential Evolution and its application in its newest formulation.
Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Presents a comprehensive comparison of the performance of stochastic optimization algorithms / Includes an introduction to benchmarking and statistical analysis / Provides a web-based tool for making statistical comparisons of optimization algorithms / Overviews of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches.
Current aopics in artificial intelligence ; 12th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2007, Salamanca, Spain, November 12-16, 2007, Selected Papers
The book presented address all current issues of artificial intelligence ranging from methodological and foundational aspects to advanced applications in various fields.
Current aopics in artificial intelligence ; 11th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2005, Santiago de Compostela, Spain, November 16-18, 2005, Revised Selected Papers
This book constitutes the thoroughly refered post-proceedings of the 11th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2005, held in Santiago de Compostela, Spain in November 2005.
Computers and Games ; 6th International Conference, CG 2008, Beijing, China, September 29 - October 1, 2008. Proceedings
This book constitutes the refereed proceedings of the 6th International Conference on Computers and Games, CG 2008, held in Beijing, China, in September/October 2008 co-located with the 13th Computer Olympiad and the 16th World Computer-Chess Championship.
Computer Science Logic ; 21 International Workshop, CSL 2007, 16th Annual Conference of the EACSL, Lausanne, Switzerland, September 11-15, 2007, Proceedings
This book covers logic and games, expressiveness, games and trees, logic and deduction, lambda calculus, finite model theory, linear logic, proof theory, and game semantics.
Computational Intelligence in Reliability Engineering : New Metaheuristics, Neural and Fuzzy Techniques in Reliability
This volume contains chapters presenting applications of different metaheuristics (ant colony optimization, great deluge algorithm, cross-entropy method and particle swarm optimization) in reliability engineering. It also includes chapters devoted to cellular automata and support vector machines and different applications of artificial neural networks, a powerful adaptive technique that can be used for learning, prediction and optimization. Several chapters describe different aspects of imprecise reliability and applications of fuzzy and vague set theory.
