This volume contains the best carefully revised full papers selected from the presentations accepted for the AI '93 and AI '94 Workshop on Evolutionary Computation held in Australia.
The 21 papers included cover a wide range of topics in the field of evolutionary computation, from constrained function optimization to combinatorial optimization, from evolutionary programming to genetic programming, from robotic strategy learning to co-evolutionary game strategy learning. The papers reflect important recent progress in the field; more than half of the papers come from overseas.

The effect of function noise on GP efficiency.- Genetic approaches to learning recursive relations.- An application of Genetic Programming to the 4-Op problem using map-trees.- Direct replacement: A Genetic Algorithm without mutation which avoids deception.- Competitive evolution: A natural approach to operator selection.- Emergent collective computational abilities in interacting particle systems.- A perspective on evolutionary computation.- An experimental study of N-Person Iterated Prisoner's Dilemma games.- A systolic architecture for high speed hypergraph partitioning using a genetic algorithm.- Development of hybrid optimisation techniques based on genetic algorithms and simulated annealing.- Development of parallel hybrid optimisation techniques based on genetic algorithms and simulated annealing.- Genetic algorithms for cutting stock problems: With and without contiguity.- GASBOR: A genetic algorithm for switchbox routing in integrated circuits.- The calculus of self-modifiable algorithm based evolutionary computer network routing.- Evolving robot strategy for open ended game.- An evolutionary approach to adaptive model-building.- Training neural networks with influence diagrams.- A behavioural theory of intelligent machines as a framework for the analysis of adaptation.- On evolving robust strategies for iterated prisoner's dilemma.- Comparison of heuristic search algorithms for single machine scheduling problems.- Encoding graphs for genetic algorithms: An investigation using the minimum spanning tree problem.