Evolutionary Computation: Introduction to evolutioninspired computing models.

Genetic Programming: Examines adaptive systems for evolving programs.

Genetic Algorithm: Analyzes the power of genetic optimization techniques.

Evolutionary Algorithm: Discusses algorithms driven by biological evolution.

Bioinspired Computing: Looks at natureinspired computational models.

Evolutionary Programming: Explores simulation of evolution in problemsolving.

Crossover (Genetic Algorithm): Details gene recombination processes.

Mutation (Genetic Algorithm): Reviews mutation’s role in diversity.

Chromosome (Genetic Algorithm): Describes genetic data structures.

Metaheuristic: Explores frameworks for finding nearoptimal solutions.

Evolution Strategy: Investigates adaptive mechanisms for optimization.

Effective Fitness: Defines fitness evaluation in evolutionary contexts.

Premature Convergence: Warns of early optimization pitfalls.

Genetic Representation: Examines data encoding in genetic algorithms.

Memetic Algorithm: Covers hybrid algorithms combining genetic and local searches.

Humanbased Computation: Reviews human influence in computation.

Lateral Computing: Examines lateral interactions in computational systems.

Natural Computing: Explores computing grounded in natural processes.

Artificial Life: Introduces lifelike systems and their applications.

Soft Computing: Investigates flexible, approximate computation methods.

Neuroevolution of Augmenting Topologies: Delves into evolving neural networks.