8 A Review of Major Application Areas of Differential Evolution <s> Introduction <s> Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect. Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository. <s> BIB001 </s> 8 A Review of Major Application Areas of Differential Evolution <s> Introduction <s> From the Publisher: ::: In this revised and significantly expanded second edition, distinguished scientist David B. Fogel presents the latest advances in both the theory and practice of evolutionary computation to help you keep pace with developments in this fast-changing field.. "In-depth and updated, Evolutionary Computation shows you how to use simulated evolution to achieve machine intelligence. You will gain current insights into the history of evolutionary computation and the newest theories shaping research. Fogel carefully reviews the "no free lunch theorem" and discusses new theoretical findings that challenge some of the mathematical foundations of simulated evolution. This second edition also presents the latest game-playing techniques that combine evolutionary algorithms with neural networks, including their success in playing competitive checkers. Chapter by chapter, this comprehensive book highlights the relationship between learning and intelligence.. "Evolutionary Computation features an unparalleled integration of history with state-of-the-art theory and practice for engineers, professors, and graduate students of evolutionary computation and computer science who need to keep up-to-date in this developing field. <s> BIB002 </s> 8 A Review of Major Application Areas of Differential Evolution <s> Introduction <s> A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described. <s> BIB003 </s> 8 A Review of Major Application Areas of Differential Evolution <s> Introduction <s> A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. ________________________________________ 1)International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: 510-643-7684. E-mail: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn-Ring 6, D-81739 Muenchen, Germany. Fax: 01149-636-44577, Email:rainer.storn@zfe.siemens.de. 2)836 Owl Circle, Vacaville, CA 95687, kprice@solano.community.net. <s> BIB004 </s> 8 A Review of Major Application Areas of Differential Evolution <s> Introduction <s> This chapter contains sections titled: References Artificial Intelligence through a Simulation of Evolution Natural Automata and Prosthetic Devices <s> BIB005
Evolutionary Algorithms (EAs) refer to problem solving optimization algorithms which employ computational models of evolutionary processes. A variety of evolutionary algorithms have been proposed. The major ones include: Genetic Algorithms   , Evolutionary Programming BIB002 BIB005 , Evolution Strategies  , Genetic Programming BIB001 , Particle Swarm Optimization BIB003 , and the Differential Evolution algorithm BIB004 . All these algorithms share the common conceptual base of simulating the evolution of a population of individuals using a predefined set of operators. Commonly two kinds of operators are used: the selection and the search operators. The most widely used search operators are mutation and recombination. The selection operator enforces the natural selection and the survival of the fittest on the population of individuals. The recombination and the mutation operators stochastically perturb the individuals providing efficient exploration of the search space. This 8 perturbation is primarily controlled by the user defined recombination and mutation rates. Although simplistic from a biologist's point of view, these algorithms are sufficiently complex to provide robust and powerful search mechanisms and have shown their strength in solving hard optimization problems. In this chapter we focus on the Differential Evolution algorithm and present some of its applications, including single and multiobjective function optimization, neural network training, clustering, and real life DNA microarray problems. Furthermore, we discuss promising future extensions of the algorithm by the incorporation of genetically programmed mutation operators.