Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Stability of the chemotactic dynamics in bacterial foraging optimization algorithm
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Adaptive computational chemotaxis in bacterial foraging optimization: an analysis
IEEE Transactions on Evolutionary Computation
On stability of the chemotactic dynamics in bacterial-foraging optimization algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Individual-based modeling of bacterial foraging with quorum sensing in a time-varying environment
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Stability analysis of the reproduction operator in bacterial foraging optimization
Theoretical Computer Science
Option model calibration using a bacterial foraging optimization algorithm
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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This paper presents the study of modelling bacterial foraging behaviours in varying environments. The purpose of the study is to investigate a novel biologically inspired methodology for complex system modelling and computation, particularly for optimisation of complex dynamic systems, although this paper is mainly concerned with a novel modelling methodology. Our study focuses on the use of individual-based modelling (IbM) method to simulate the activities of bacteria and the evolvement of bacterial colonies. For this study, an architecture and a mathematical framework are designed to model bacterial foraging patterns. Under this architecture, the interactions between the environment and bacteria are investigated. A bacterial chemotaxis algorithm is derived in the framework and simulation studies are undertaken to evaluate this algorithm. The simulation results show that the proposed algorithm can reflect the bacterial behaviours and population evolution in varying environments, and also explore its potential for optimisation of dynamic systems.