Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A parallel genetic heuristic for the quadratic assignment problem
Proceedings of the third international conference on Genetic algorithms
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Comparative Study of Steady State and Generational Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Adaptive evolutionary planner/navigator for mobile robots
IEEE Transactions on Evolutionary Computation
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Recently, there has been an increasing interest in applying evolutionary computation to path planning [15]. To date, these evolutionary path planners have been single agent planners. In real-world environments where the knowledge of obstacles is naturally distributed, it is possible for single agent path planners to become overwhelmed by the volume of information needed to be processed in order to develop accurate paths quickly in non-stationary environments. In this paper, a new adaptive replacement strategy (ARS) is presented that allows steady-state evolutionary path planners to search efficiently in non-stationary environments. We compare this new ARS with another ARS using a test suite of 5 non-stationary path planning problems. Both of replacement strategies compared in this paper work by allowing an influx of diversity rather than increasing mutation rates. We refer to this influx of diversity as hyper-diversity.