Nature-Inspired Algorithms for Optimisation

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.