Genetic Algorithms

Advanced programming

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection

Please first review the characteristics of algorithms.

Start here to understand genetic algorithms

• Brute force approach
• Combinatorial optimization
• Computational intractability
• Convergence
• Crossover / crossover operator
• Elitism
• Exploration vs exploitation
• Fitness / fitness function / fitness landscape
• Heuristic
• Hill climbing
• Initialization parameters
• Local extrema
• Mating pool
• Mutation / mutation rate
• Novelty search
• Offspring
• Optimization
• Population
• Premature convergence
• Problem space
• Ranking
• Roulette wheel selection
• Selection strategy
• Simulated annealing
• Stochastic universal sampling
• Termination condition
• Tour
• Tournament selection
• Truncation selection