Titlebook: Nature-Inspired Algorithms for Optimisation; Raymond Chiong Book 2009 Springer-Verlag Berlin Heidelberg 2009 algorithm.algorithms.artifici

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Ascendancy

Antonio J. Nebro,Juan J. Durilloincludes scattering/collisions in the gaseous phase. It also includes thermal agitation, tunneling and relaxation in the liquid and solid phases. Classical mechanics, classical statistical mechanics, classical relativity and quantum mechanics are all implicated. ‘Semiclassical‘ essentially means tha

責(zé)難

ccurate nonadiabatic molecular dynamics with and without cla.This book shows how to derive the simple and accurate semiclassical methods analytically and its applications to excited-state molecular dynamics and spectroscopy simulation with and without classical trajectories. It consists of eight cha

呼吸

Why Is Optimization Difficult? issues include premature convergence, ruggedness, causality, deceptiveness, neutrality, epistasis, robustness, overfitting, oversimplification, multi-objectivity, dynamic fitness, the No Free Lunch Theorem, etc. We explain why these issues make optimization problems hard to solve and present some p

Adjourn

Visual-Acuity

The Evolutionary-Gradient-Search Procedure in Theory and Practiceents a hybrid, called the evolutionary-gradient-search procedure, that uses evolutionary variations to estimate the gradient direction in which it then performs an optimization step. Both standard benchmarks and theoretical analyses suggest that this hybrid yields superior performance. In addition,

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使隔離

A Model-Assisted Memetic Algorithm for Expensive Optimization Problemsches such as a combined global-local, modelling and memetic optimization. However, compared to existing studies it offers three novelties: a statistically-sound framework for selecting optimal models during both the global and the local search, an improved trust-region framework and a procedure for

衰老

A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large t task in optimization domain. Various algorithms have been proposed to tackle this challenging problem, but the use of estimation of distribution algorithms (EDAs) to it is rare. This chapter aims at investigating the behavior and performances of uni-variate EDAs mixed with different kernel probabi

Tdd526

Differential Evolution with Fitness Diversity Self-adaptatione factor and crossover rate are encoded within each genotype and self-adaptively updated during the evolution by means of a probabilistic criterion which takes into account the diversity properties of the entire population. The population size is also adaptively controlled by means of a novel techni