From the Publisher:
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. · Comprehensive coverage of this growing area of research · Carefully introduces each algorithm with examples and in-depth discussion · Includes many applications to real-world problems, including engineering design and scheduling · Includes discussion of advanced topics and future research · Features exercises and solutions, enabling use as a course text or for self-study · Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

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Multi-Objective Optimization Using Evolutionary Algorithms

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

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Metaheuristics in combinatorial optimization: Overview and conceptual comparison

Abst ract . T he success of binary-coded gene t ic algorithms (GA s) in problems having discrete sear ch space largely depends on the coding used to represent the prob lem var iables and on the crossover ope ra tor that propagates buildin g blocks from parent strings to children st rings . In solving optimization problems having continuous search space, binary-coded GAs discr et ize the search space by using a coding of the problem var iables in binary strings. However , t he coding of realvalued vari ables in finit e-length st rings causes a number of difficulties: inability to achieve arbit rary pr ecision in the obtained solution , fixed mapping of problem var iab les, inh eren t Hamming cliff problem associated wit h binary coding, and processing of Holland 's schemata in cont inuous search space. Although a number of real-coded GAs are developed to solve optimization problems having a cont inuous search space, the search powers of these crossover operators are not adequate . In t his paper , t he search power of a crossover operator is defined in terms of the probability of creating an arbitrary child solut ion from a given pair of parent solutions . Motivated by the success of binarycoded GAs in discrete search space problems , we develop a real-coded crossover (which we call the simulated binar y crossover , or SBX) operator whose search power is similar to that of the single-point crossover used in binary-coded GAs . Simulation results on a nu mber of realvalued test problems of varying difficulty and dimensionality suggest t hat the real-cod ed GAs with the SBX operator ar e ab le to perfor m as good or bet ter than binary-cod ed GAs wit h the single-po int crossover. SBX is found to be particularly useful in problems having mult ip le optimal solutions with a narrow global basin an d in prob lems where the lower and upper bo unds of the global optimum are not known a priori. Further , a simulation on a two-var iable blocked function shows that the real-coded GA with SBX work s as suggested by Goldberg

Simulated Binary Crossover for Continuous Search Space.

In this paper we introduce interval-schemata as a tool for analyzing real-coded genetic algorithms (GAs). We show how interval-schemata are analogous to Holland's symbol-schemata and provide a key to understanding the implicit parallelism of real-valued GAs. We also show how they support the intuition that real-coded GAs should have an advantage over binary coded GAs in exploiting local continuities in function optimization. On the basis of our analysis we predict some failure modes for real-coded GAs using several different crossover operators and present some experimental results that support these predictions. We also introduce a crossover operator for real-coded GAs that is able to avoid some of these failure modes.

Real-Coded Genetic Algorithms and Interval-Schemata

Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation.

Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.

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Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis

Intrinsic parallelism is shown to have application beyond schemata and o-schemata. More general objects called formae are introduced and general operators which manipulate these are introduced and discussed. These include random, respectful recombination. The extended formalism is applied to various common representations and standard operators are analysed in the light of the formalism.

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Nicholas J. Radcliffe

Papers

Forma Analysis and Random Respectful Recombination.