Nature-Inspired Algorithms for Optimisation - Chiong, Raymond; SpringerLink (Online service) (Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009)
Section I: Introduction -- Why Is Optimization Difficult? -- The Rationale Behind Seeking Inspiration from Nature -- Section II: Evolutionary Intelligence -- The Evolutionary-Gradient-Search Procedure in Theory and Practice -- The Evolutionary Transition Algorithm: Evolving Complex Solutions Out of Simpler Ones -- A Model-Assisted Memetic Algorithm for Expensive Optimization Problems -- A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization -- Differential Evolution with Fitness Diversity Self-adaptation -- Central Pattern Generators: Optimisation and Application -- Section III: Collective Intelligence -- Fish School Search -- Magnifier Particle Swarm Optimization -- Improved Particle Swarm Optimization in Constrained Numerical Search Spaces -- Applying River Formation Dynamics to Solve NP-Complete Problems -- Section IV: Social-Natural Intelligence -- Algorithms Inspired in Social Phenomena -- Artificial Immune Systems for Optimization -- Section V: Multi-Objective Optimisation -- Ranking Methods in Many-Objective Evolutionary Algorithms -- On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II -- Improving the Performance of Multiobjective Evolutionary Optimization Algorithms Using Coevolutionary Learning -- Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz's Model with Application to the Caracas Stock Exchange.
Keywords: Applied mathematics; Engineering mathematics; Artificial intelligence; Operations research; Decision making; Mathematical and Computational Engineering; Artificial Intelligence; Operations Research/Decision Theory