Syllabus
Evolutionary programming, finite automata, meta-evolution.
Evolution Strategie, cooperation of individuals, 1+1, m+1 algorithms.
Genetic programming, LISP tree reprezentations, modularity.
Open-ended evolution, adaptive behavior, artificial life simulations, emergence (Tierra, Avida, Framsticks)
Numerical optimization, binary vs. float reprezentations, constraints handling.
Combinatorial problems, knapsack, travelling salesman, representations, operators.
Neural networks evolution, internal representation, topology, weights learning, hybrid algorithms.
Annotation
Evolutionary programming, evolutionary strategies, genetic programming. Open-ended evolution and artificial life.
Binary vs. float evolutionary algorithms, numerical optimization. Constraint handling, combinatorial optimization.
Evolutionary learning of neural networks.