Hardware Evolution: Automatic Design of Electronic Circuits in Reconfigurable Hardware by Artificial Evolution
Error Thresholds and Their Relation to Optimal Mutation Rates
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Multi-phase sumo maneuver learning
Robotica
A new approach to control a population of mobile robots using genetic programming
Proceedings of the 2008 ACM symposium on Applied computing
Stability of Coordination Requires Mutuality of Interaction in a Model of Embodied Agents
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Adaptive Behavioural Modulation and Hysteresis in an Analogue of a Kite Control Task
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Associative Learning on a Continuum in Evolved Dynamical Neural Networks
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
The dynamics of associative learning in an evolved situated agent
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Evolution of neural networks for active control of tethered airfoils
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Evolutionary humanoid robotics: past, present and future
50 years of artificial intelligence
Evolutionary optimization of state selective field ionization for quantum computing
Applied Soft Computing
The microbial genetic algorithm
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Synapsing variable length crossover: an algorithm for crossing and comparing variable length genomes
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
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I start with a basic tutorial on Artificial Evolution, and then show the simplest possible way of implementing this with the Microbial Genetic Algorithm. I then discuss some shortcomings in many of the basic assumptions of the orthodox Genetic Algorithm (GA) community, and give a rather different perspective. The basic principles of SAGA (Species Adaptation GAs) will be outlined, and the concept of Neutral Networks, pathways of level fitness through a fitness landscape will be introduced. A practical example will demonstrate the relevance of this.