Chasing chaos

Johnny Kelsey; Jon Timmis; Andrew Hone

doi:10.1109/CEC.2003.1299605

Chasing chaos

Johnny Kelsey, Jon Timmis, Andrew Hone

Department of Computer Science

University of Kent

Research output: Contribution to conference › Paper › peer-review

15 Citations (Scopus)

Abstract

Both simple and hybrid genetic algorithms encounter difficulties when presented with a function which has multiple values. Similarly, changing environments or functions which change rapidly present other problems. This paper presents an algorithm that is capable of coping with both of these scenarios: it can accommodate multiple solutions simultaneously and can track changes in optima efficiently. The proposed B-cell algorithm is inspired by the natural immune system, which itself displays similar capabilities of tracking multiple, moving targets in the form of infectious agents. This paper employs two nonlinear mappings which display chaotic behaviour to demonstrate the effectiveness of the B-cell algorithm in tracking multiple, moving targets. A number of experiments are conducted and results reported from the B-cell algorithm and standard hybrid genetic algorithm approaches. These results show the benefit of the B-cell algorithm approach when compared against these heuristic approaches.

Original language	English
Pages	413-419
Number of pages	7
DOIs	https://doi.org/10.1109/CEC.2003.1299605
Publication status	Published - 2003
Event	2003 Congress on Evolutionary Computation, CEC 2003 - Canberra, ACT, Australia Duration: 08 Dec 2003 → 12 Dec 2003

Conference

Conference	2003 Congress on Evolutionary Computation, CEC 2003
Country/Territory	Australia
City	Canberra, ACT
Period	08 Dec 2003 → 12 Dec 2003

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title = "Chasing chaos",

abstract = "Both simple and hybrid genetic algorithms encounter difficulties when presented with a function which has multiple values. Similarly, changing environments or functions which change rapidly present other problems. This paper presents an algorithm that is capable of coping with both of these scenarios: it can accommodate multiple solutions simultaneously and can track changes in optima efficiently. The proposed B-cell algorithm is inspired by the natural immune system, which itself displays similar capabilities of tracking multiple, moving targets in the form of infectious agents. This paper employs two nonlinear mappings which display chaotic behaviour to demonstrate the effectiveness of the B-cell algorithm in tracking multiple, moving targets. A number of experiments are conducted and results reported from the B-cell algorithm and standard hybrid genetic algorithm approaches. These results show the benefit of the B-cell algorithm approach when compared against these heuristic approaches.",

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AU - Timmis, Jon

AU - Hone, Andrew

PY - 2003

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AB - Both simple and hybrid genetic algorithms encounter difficulties when presented with a function which has multiple values. Similarly, changing environments or functions which change rapidly present other problems. This paper presents an algorithm that is capable of coping with both of these scenarios: it can accommodate multiple solutions simultaneously and can track changes in optima efficiently. The proposed B-cell algorithm is inspired by the natural immune system, which itself displays similar capabilities of tracking multiple, moving targets in the form of infectious agents. This paper employs two nonlinear mappings which display chaotic behaviour to demonstrate the effectiveness of the B-cell algorithm in tracking multiple, moving targets. A number of experiments are conducted and results reported from the B-cell algorithm and standard hybrid genetic algorithm approaches. These results show the benefit of the B-cell algorithm approach when compared against these heuristic approaches.

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T2 - 2003 Congress on Evolutionary Computation, CEC 2003

Y2 - 8 December 2003 through 12 December 2003

ER -

Chasing chaos

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