Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Observing the swarm behaviour during its evolutionary design
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Fast bio-inspired computation using a GPU-based systemic computer
Parallel Computing
Systemic computation using graphics processors
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
ACO with tabu search on a GPU for solving QAPs using move-cost adjusted thread assignment
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Bitwise operations for GPU implementation of genetic algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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This paper compares three common evolutionary algorithms and our modified GA, a Distributed Adaptive Genetic Algorithm (DAGA). The optimal approach is sought to adapt, in near real-time, biological model behaviour to that of real biology within a laboratory. Near real-time adaptation is achieved with a Graphics Processing Unit (GPU). This, together with evolutionary computation, enables new forms of experimentation such as online testing, where biology and computational model are simultaneously stimulated and their responses compared. Rapid analysis and validation provide a platform that is required for rapid prototyping, and along with online testing, can provide new insight into the cause of biological behaviour. In this context, results demonstrate that our DAGA implementation is more efficient than the other three evolutionary algorithms due to its suitability to the adaptation environment, namely the large population sizes promoted by the GPU architecture.