Binary Genetic Algorithm (GA)

This is a series of videos about binary genetic algorithm (GA), which will cover the working concepts and principles and the details of each components of binary GA (i.e., coding/encoding prcoess for binary representation, decision variables, cost functions, population, natural selection, selection, genetic operations (crossover and mulation), stopping criteria, performance evaluation, benchmark functions, schema theorem), using flowcharts, diagrams, animations and examples.

Part 1: Introduction of Binary Genetic Algorithm

Part 2: General Working Principle and Coding/Encoding of Binary Genetic Algorithm

Part 3: Decision Variables, Cost Functions and Population and Natural Selection Process of Binary Genetic Algorithm

Part 4: Selection Processes (Pairing, Random Pairing, Cost Weighting and Tournament) of Binary Genetic Algorithm

Part 5: Crossover and Mutation of Binary Genetic Algorithm

Part 6:  Stopping Criteria, Performance Evaluation and Benchmark Functions of Binary Genetic Algorithm

Part 7: Why do GAs work, Schema Theorem of Binary Genetic Algorithm