Evolutionary Algorithms in Engineering and Computer Science

M. M. Makela





Evolutionary Algorithms in Engineering and Computer Science Edited by K. Miettinen, University of Jyv䳫yl䬠Finland M. M. M䫥l䬠University of Jyv䳫yl䬠Finland P. Neittaanm䫩, University of Jyv䳫yl䬠Finland J. P鲩aux, Dassault Aviation, France What is Evolutionary Computing? Based on the genetic message encoded in DNA, and digitalized algorithms inspired by the Darwinian framework of evolution by natural selection, Evolutionary Computing is one of the most important information technologies of our times. Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming. In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. Furthermore, these algorithms can easily be hybridized with traditional optimization techniques. This book presents state-of-the-art lectures delivered by international academic and industrial experts in the field of evolutionary computing. It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics. This rapidly growing field, with its deep understanding and assesssment of complex problems in current practice, provides an effective, modern engineering tool. This book will therefore be of significant interest and value to all postgraduates, research scientists and practitioners facing complex optimization problems.         

Table of Contents


Using Genetic Algorithms for Optimization: Technology Transfer in Action (J. Haataja).

An Introduction to Evolutionary Computation and Some Applications (D. Fogel).

Evolutionary Computation: Recent Developments and Open Issues (K. De Jong).

Some Recent Important Foundational Results in Evolutionary Computation (D. Fogel). Evolutionary Algorithms for Engineering Applications (Z. Michalewicz, et al.).

Embedded Path Tracing and Neighbourhood Search Techniques (C. Reeves T. Yamada). Parallel and Distributed Evolutionary Algorithms (M. Tomassini).

Evolutionary Multi-Criterion Optimization (K. Deb).

ACO Algorithms for the Traveling Salesman Problem (T. St?M. Dorigo).

Genetic Programming: Turing's Third Way to Achieve Machine Intelligence (J. Koza, et al.).

Automatic Synthesis of the Topology and Sizing for Analog Electrical Circuits Using Genetic Programming (F. Bennett, et al.).


Multidisciplinary Hybrid Constrained GA Optimization (G. Dulikravich, et al.).

Genetic Algorithm as a Tool for Solving Electrical Engineering Problems (M. Rudnicki, et al.).

Genetic Algorithms in Shape Optimization: Finite and Boundary Element Applications (M. Cerrolaza W. Annicchiarico).

Genetic Algorithms and Fractals (E. Lutton).

Three Evolutionary Approaches to Clustering (H. Luchian).


Evolutionary Algorithms Applied to Academic and Industrial Test Cases (T. B䣫, et al.).

Optimization of an Active Noise Control System Inside an Aircraft, Based on the Simultaneous Optimal Positioning of Microphones and Speakers, with the Use of a Genetic Algorithm (Z. Diamantis, et al.).

Generator Scheduling in Power Systems by Genetic Algorithm and Expert System (B. Galvan, et al.).

Efficient Partitioning Methods for 3-D Unstructured Grids Using Genetic Algorithms (A. Giotis, et al.).

Genetic Algorithms in Shape Optimization of a Paper Machine Headbox (J. H䭤l䩮en, et al.).

A Parallel Genetic Algorithm for Multi-Objective Optimization in Computational Fluid Dynamics (N. Marco, et al.).

Application of a Multi Objective Genetic Algorithm and a Neural Network to the Optimisation of Foundry Processes (G. Meneghetti, et al.).

Circuit Partitioning Using Evolution Algorithms (J. Montiel-Nelson, et al.).




《演化算法在工程和計算機科學中的應用》由芬蘭約伊維爾卡大學的K. Miettinen、M. M. M䫥l和P. Neittaanm䫩以及法國達索航空的J. P鲩aux共同編輯。什麼是演化計算?基於DNA中編碼的遺傳信息,以及受到達爾文進化選擇框架啟發的數字化算法,演化計算是我們時代最重要的信息技術之一。演化算法包括所有自適應和計算模型,如遺傳算法、進化策略、演化編程和遺傳編程。此外,它們在全局優化問題的搜索中表現良好,可以生產出堅固且易於實施的優化軟件。此外,這些算法可以輕鬆與傳統優化技術混合使用。本書介紹了國際學術和工業專家在演化計算領域的最新講座。它將人工智能和科學計算與應用導向行業(如航空航天、電子、電信、能源和經濟)中遇到的現實問題相結合。這個快速發展的領域通過對當前實踐中複雜問題的深入理解和評估,提供了一種有效的現代工程工具。因此,本書對所有研究生、研究科學家和面臨複雜優化問題的從業人員都具有重要的興趣和價值。



使用遺傳算法進行優化:技術轉移實踐(J. Haataja)。

演化計算簡介及其應用(D. Fogel)。

演化計算:最新發展和開放問題(K. De Jong)。

演化計算中的一些重要基礎結果(D. Fogel)。

工程應用的演化算法(Z. Michalewicz等)。

嵌入式路徑追踪和鄰域搜索技術(C. Reeves T. Yamada)。

並行和分佈式演化算法(M. Tomassini)。

演化多目標優化(K. Deb)。

螞蟻優化算法應用於旅行商問題(T. St?M. Dorigo)。

遺傳編程:實現機器智能的圖靈第三種方式(J. Koza等)。

使用遺傳編程自動合成類比電路的拓撲和尺寸(F. Bennett等)。


多學科混合約束遺傳算法優化(G. Dulikravich等)。

遺傳算法作為解決電氣工程問題的工具(M. Rudnicki等)。

遺傳算法在形狀優化中的應用:有限元和邊界元應用(M. Cerrolaza W. Annicchiarico)。

遺傳算法和分形(E. Lutton)。

三種演化方法應用於聚類(H. Luchian)。


演化算法應用於學術和工業測試案例(T. B䣫等)。

基於遺傳算法的飛機內主動噪音控制系統優化,基於麥克風和揚聲器的同時最佳定位,使用遺傳算法(Z. Diamantis等)。

遺傳算法在電力系統中的發電機調度(T. B䣫等)。