Johnny Kelsey - Academia.edu (original) (raw)
Papers by Johnny Kelsey
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
Do Artificial Immune Systems (AIS) have something to offer the world of optimisation? Indeed do t... more Do Artificial Immune Systems (AIS) have something to offer the world of optimisation? Indeed do they have any new to offer at all? This paper reports the initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part of ongoing research which forms part of a larger project to assess the performance and viability of AIS. The investigation employs standard benchmark functions, and demonstrates that for these functions the opt-aiNET algorithm, when compared to the Bcell algorithm and hybrid GA, on average, takes longer to find the solution, without necessarily a better quality solution. Reasons for these differences are proposed and it is acknowledge that this is preliminary empirical work. It is felt that a more theoretical approach may well be required to ascertain real performance and applicability issues.
Abstract. When considering function optimisation, there is a trade off between quality of solutio... more Abstract. When considering function optimisation, there is a trade off between quality of solutions and the number of evaluations it takes to find that solution. Hybrid genetic algorithms have been widely used for function optimisation and have been shown to perform extremely well on these tasks. This paper presents a novel algorithm inspired by the mammalian immune system, combined with a unique mutation mechanism. Results are presented for the optimisation of twelve functions, ranging in dimensionality from one to twenty. Results show that the immune inspired algorithm performs significantly fewer evaluations when compared to a hybrid genetic algorithm, whilst not sacrificing quality of the solution obtained. 1
The 2003 Congress on Evolutionary Computation, 2003. CEC '03., 2003
... Here we present a brief overview of a novel algorithm, called the B-cell algorithm (BCA). The... more ... Here we present a brief overview of a novel algorithm, called the B-cell algorithm (BCA). The algorithm is pre-sented in its entirety in [Kelsey and Timmis 20031. ... 416 Page 5. 20 is fed into the equation and the output is then fed hack into the equation to produce the next output. ...
Artificial Immune Systems, 2008
... Johnny Kelsey1, Brian Henderson2, Rob Seymour3, and Andy Hone4 1 CoMPLEX, University College ... more ... Johnny Kelsey1, Brian Henderson2, Rob Seymour3, and Andy Hone4 1 CoMPLEX, University College London 2 Division of Microbial Diseases, University College London 3 CoMPLEX/ Department of Mathematics, University College London 4 IMSAS, University of Kent ...
Artificial Immune Systems, 2004
... Systems Andrew Hone1 and Johnny Kelsey2 ... can be derived from an action of the form (10), a... more ... Systems Andrew Hone1 and Johnny Kelsey2 ... can be derived from an action of the form (10), as can Einstein's general theory of relativity [4]. The field theoretic action (10) is central to the path integral formulation of quantum field theory, as pioneered by Feynman [7]. The path ...
Genetic and Evolutionary ComputationGECCO …, 2003
When considering function optimisation, there is a trade off between quality of solutions and the... more When considering function optimisation, there is a trade off between quality of solutions and the number of evaluations it takes to find that solution. Hybrid genetic algorithms have been widely used for function optimisation and have been shown to perform extremely well on these tasks. This paper presents a novel algorithm inspired by the mammalian immune system, combined with a unique mutation mechanism. Results are presented for the optimisation of twelve functions, ranging in dimensionality from one to twenty. Results show that the immune inspired algorithm performs significantly fewer evaluations when compared to a hybrid genetic algorithm, whilst not sacrificing quality of the solution obtained.
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
Do Artificial Immune Systems (AIS) have something to offer the world of optimisation? Indeed do t... more Do Artificial Immune Systems (AIS) have something to offer the world of optimisation? Indeed do they have any new to offer at all? This paper reports the initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part of ongoing research which forms part of a larger project to assess the performance and viability of AIS. The investigation employs standard benchmark functions, and demonstrates that for these functions the opt-aiNET algorithm, when compared to the Bcell algorithm and hybrid GA, on average, takes longer to find the solution, without necessarily a better quality solution. Reasons for these differences are proposed and it is acknowledge that this is preliminary empirical work. It is felt that a more theoretical approach may well be required to ascertain real performance and applicability issues.
Abstract. When considering function optimisation, there is a trade off between quality of solutio... more Abstract. When considering function optimisation, there is a trade off between quality of solutions and the number of evaluations it takes to find that solution. Hybrid genetic algorithms have been widely used for function optimisation and have been shown to perform extremely well on these tasks. This paper presents a novel algorithm inspired by the mammalian immune system, combined with a unique mutation mechanism. Results are presented for the optimisation of twelve functions, ranging in dimensionality from one to twenty. Results show that the immune inspired algorithm performs significantly fewer evaluations when compared to a hybrid genetic algorithm, whilst not sacrificing quality of the solution obtained. 1
The 2003 Congress on Evolutionary Computation, 2003. CEC '03., 2003
... Here we present a brief overview of a novel algorithm, called the B-cell algorithm (BCA). The... more ... Here we present a brief overview of a novel algorithm, called the B-cell algorithm (BCA). The algorithm is pre-sented in its entirety in [Kelsey and Timmis 20031. ... 416 Page 5. 20 is fed into the equation and the output is then fed hack into the equation to produce the next output. ...
Artificial Immune Systems, 2008
... Johnny Kelsey1, Brian Henderson2, Rob Seymour3, and Andy Hone4 1 CoMPLEX, University College ... more ... Johnny Kelsey1, Brian Henderson2, Rob Seymour3, and Andy Hone4 1 CoMPLEX, University College London 2 Division of Microbial Diseases, University College London 3 CoMPLEX/ Department of Mathematics, University College London 4 IMSAS, University of Kent ...
Artificial Immune Systems, 2004
... Systems Andrew Hone1 and Johnny Kelsey2 ... can be derived from an action of the form (10), a... more ... Systems Andrew Hone1 and Johnny Kelsey2 ... can be derived from an action of the form (10), as can Einstein's general theory of relativity [4]. The field theoretic action (10) is central to the path integral formulation of quantum field theory, as pioneered by Feynman [7]. The path ...
Genetic and Evolutionary ComputationGECCO …, 2003
When considering function optimisation, there is a trade off between quality of solutions and the... more When considering function optimisation, there is a trade off between quality of solutions and the number of evaluations it takes to find that solution. Hybrid genetic algorithms have been widely used for function optimisation and have been shown to perform extremely well on these tasks. This paper presents a novel algorithm inspired by the mammalian immune system, combined with a unique mutation mechanism. Results are presented for the optimisation of twelve functions, ranging in dimensionality from one to twenty. Results show that the immune inspired algorithm performs significantly fewer evaluations when compared to a hybrid genetic algorithm, whilst not sacrificing quality of the solution obtained.