Evolutionary Algorithms

Introduction, Components of Evolutionary Algorithms – Representation, Evaluation Function (Fitness Function), Population, Parent Selection Mechanism, Variation Operators, Survivor Selection, Mechanism (Replacement), Initialization, Termination Condition. Example Applications - The 8-Queens Problem, The Knapsack Problem. Working of an Evolutionary Algorithm (EA), Evolutionary Computing and Global Optimization.

Representation of Individuals, Mutation and Recombination – Binary, Integer, Real- valued or Floating-Point, Permutation Representations, Multiparent recombination. Population Models, Parent Selection - Fitness Proportional Selection, Ranking Selection, Implementing Selection Probabilities, Tournament Selection. Survivor Selection - Age-Based Replacement, Fitness Based Replacement. Example Application: Solving a Job Shop Scheduling Problem.

Evolution Strategy operators – Selection, Crossover, Mutation. A generic Evolution strategy Algorithm. Examples of Changing Parameters - Changing the Mutation Step Size. Changing the Penalty Coefficients. Classification of Control Techniques, Examples of Varying EA Parameter, Varying Several Parameters Simultaneously.

Multi-Modal Problems – Introduction. Genetic Drift, Biological Motivations and Algorithmic Approaches. Algorithmic vs. Genetic vs. Solution Space. Multi-Objective Evolutionary Algorithms – Example. Dominance and Pareto optimality. EA Approaches to Multi-Objective Optimization. Example Application: Distributed Co- Evolution of Job-shop Schedules.

Co-evolution - Cooperative co-evolution. Competitive co-evolution, Example Application: Co-evolutionary Constraint Satisfaction. Interactive Evolution – Optimization. Design, Exploration. Interactive Evolutionary Design and Art, Example Application - The Mondriaan Evolver. Non-Stationary Function Optimization - Algorithmic Approaches, Selection and Replacement Policies. Example Application - The Time Varying Knapsack Problem.