Why Is Optimization Difficult?
Introduction
{\it Basic Terminology}
{\it The Term βDifficult?}
Premature Convergence
{\it Introduction}
{\it The Problem}
{\it One Cause: Loss of Diversity}
{\it Countermeasures}
Ruggedness and Weak Causality
{\it The Problem: Ruggedness}
{\it One Cause: Weak Causality}
{\it Countermeasures}
Deceptiveness
{\it Introduction}
{\it The Problem}
{\it Countermeasures}
Neutrality and Redundancy
{\it The Problem: Neutrality}
{\it Evolvability}
{\it Neutrality: Problematic and Beneficial}
{\it Redundancy: Problematic and Beneficial}
{\it Summary}
Epistasis
{\it Introduction}
{\it The Problem}
{\it Countermeasures}
Noise and Robustness
{\it Introduction ? Noise}
{\it The Problem: Need for Robustness}
{\it Countermeasures}
Overfitting and Oversimplification
{\it Overfitting}
{\it Oversimplification}
Multi-objective Optimization
{\it Introduction}
{\it The Problem}
{\it Countermeasures}
{\it Constraint Handling}
Dynamically Changing Fitness Landscape
The No Free Lunch Theorem
Concluding Remarks
References
The Rationale Behind Seeking Inspiration from Nature
References
The Evolutionary-Gradient-Search Procedure in Theory and Practice
References
The Evolutionary Transition Algorithm: Evolving Complex Solutions Out of Simpler Ones
References
A Model-Assisted Memetic Algorithm for Expensive Optimization Problems
References
A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization
References
Differential Evolution with Fitness Diversity Self-adaptation
References
Central Pattern Generators: Optimisation and Application
References
Fish School Search
Introduction
Background
References
Magnifier Particle Swarm Optimization
References
Improved Particle Swarm Optimization in Constrained Numerical Search Spaces
References
Applying River Formation Dynamics to Solve NP-Complete Problems
Appendix
References
Algorithms Inspired in Social Phenomena
References
Artificial Immune Systems for Optimization
References
Ranking Methods in Many-Objective Evolutionary Algorithms
References
On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II
References
Improving the Performance of Multiobjective Evolutionary Optimization Algorithms Using Coevolutionary Learning
Introduction
Related Works
Multiobjective Optimization Problems
Multiobjective Evolutionary Algorithm: SPEA2
Proposed Algorithms
{\it SPEA2-CE-KR}
{\it SPEA2-CC}
Test Problems and Performance Measures
Parameters Used in the Experiments
Optimization Results
Discussion
Conclusions and Future Work
Appendix
References
Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitzβs Model with Application to the Caracas Stock Exchange
References