This book covers the following topics: The biological paradigm, Threshold logic, Weighted Networks, The Perceptron, Perceptron learning, Unsupervised learning and clustering algori...
This paper presents a comparative study of three popular, Evolutionary Algorithms (EA); Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) ...
This paper describes a collection of algorithms that we developed and implemented to facilitate the automatic recovery of the modular structure of a software system from its sourc...
Spiros Mancoridis, Brian S. Mitchell, C. Rorres, Y...
The performance of genetic programming relies mostly on population-contained variation. If the population diversity is low then there will be a greater chance of the algorithm bein...
We hypothesize that the relationship between parameter settings, specically parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programmin...