Evolutionary Computation - Harnessing Intelligent Algorithms for Advanced Robotic Systems
Fouad Sabry
Maison d'édition: One Billion Knowledgeable
Synopsis
1. Evolutionary Computation: Introduction to evolutioninspired computing models. 2. Genetic Programming: Examines adaptive systems for evolving programs. 3. Genetic Algorithm: Analyzes the power of genetic optimization techniques. 4. Evolutionary Algorithm: Discusses algorithms driven by biological evolution. 5. Bioinspired Computing: Looks at natureinspired computational models. 6. Evolutionary Programming: Explores simulation of evolution in problemsolving. 7. Crossover (Genetic Algorithm): Details gene recombination processes. 8. Mutation (Genetic Algorithm): Reviews mutation’s role in diversity. 9. Chromosome (Genetic Algorithm): Describes genetic data structures. 10. Metaheuristic: Explores frameworks for finding nearoptimal solutions. 11. Evolution Strategy: Investigates adaptive mechanisms for optimization. 12. Effective Fitness: Defines fitness evaluation in evolutionary contexts. 13. Premature Convergence: Warns of early optimization pitfalls. 14. Genetic Representation: Examines data encoding in genetic algorithms. 15. Memetic Algorithm: Covers hybrid algorithms combining genetic and local searches. 16. Humanbased Computation: Reviews human influence in computation. 17. Lateral Computing: Examines lateral interactions in computational systems. 18. Natural Computing: Explores computing grounded in natural processes. 19. Artificial Life: Introduces lifelike systems and their applications. 20. Soft Computing: Investigates flexible, approximate computation methods. 21. Neuroevolution of Augmenting Topologies: Delves into evolving neural networks.
