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Papers by James Montgomery

Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11, 2011

Computers & Operations Research, 2008

Lecture Notes in Computer Science, 2002

Lecture Notes in Computer Science, 2004

Lecture Notes in Computer Science, 2002

Proceedings of Inaugural Workshop on Artificial Life, Adelaide, Australia, 2001

Abstract. Ant colony optimisation techniques are usually guided by pheromone and heuristic cost i... more Abstract. Ant colony optimisation techniques are usually guided by pheromone and heuristic cost information when choosing the next element to add to a solution. However, while an individual element may be attractive, usually its long term consequences are neither known nor considered. For instance, a short link in a TSP may be incorporated into an ant's solution, yet, as a consequence of this link, the rest of the path may be longer than if another link was chosen. The Accumulated Experience Ant Colony uses the previous experiences ...

International Journal of Computational Intelligence and Applications, 2003

Many algorithms have been devised for tackling combinatorial optimisation problems (COPs). Tradit... more Many algorithms have been devised for tackling combinatorial optimisation problems (COPs). Traditional Operations Research (OR) techniques such as Branch and Bound and Cutting Planes Algorithms can, given enough time, guarantee an optimal solution as they explicitly exploit features of the optimisation function they are solving. Specialised heuristics exist for most COPs that also exploit features of the optimisation function to arrive at a good, but probably not optimal, solution.

Abstract The aim of the paper is to present the results of research into component based software... more Abstract The aim of the paper is to present the results of research into component based software development by providing a specification mechanism allowing searching for components in a component repository. A new component classification framework is proposed based on which a Component Description Manager has been designed and implemented.

Each particle in a swarm maintains its current position and its personal best position. It is use... more Each particle in a swarm maintains its current position and its personal best position. It is useful to think of these personal best positions as a population of attractors – updates to current positions are based on attractions to these personal best positions. If the population of attractors has high diversity, it will encourage a broad exploration of the search space with particles being drawn in many different directions. However, the population of attractors can converge quickly – attractors can draw other particles towards them, and these particles can update their own personal bests to be near the first attractor. This convergence of attractors can be reduced by having a particle update the attractor it has approached rather than its own attractor/personal best. This simple change to the update procedure in particle swarm optimization incurs minimal computational cost, and it can lead to large performance improvements in multi-modal search spaces.

In order to relate machine learning problems we argue that we need to be able to articulate what ... more In order to relate machine learning problems we argue that we need to be able to articulate what is meant by a single machine learning problem. By attempting to name the various aspects of a learning problem we hope to clarify ways in which learning problems might be related to each other. We tentatively put forward a proposal for an anatomy of learning problems that will serve as scaffolding for posing questions about relations. After surveying the way learning problems are discussed in a range of repositories and services. We then argue that the terms used to describe problems to better understand a range of viewpoints within machine learning ranging from the theoretical to the practical.

Differential evolution (DE) is a widely-effective population-based continuous optimiser that requ... more Differential evolution (DE) is a widely-effective population-based continuous optimiser that requires convergence to automatically scale its moves. However, once its population has begun to converge its ability to conduct global search is diminished, as the difference vectors used to generate new solutions are derived from the current population members' positions. In multi-modal search spaces DE may converge too rapidly, i.e., before adequately exploring the search space to identify the best region(s) in which to conduct its finer-grained search. Traditional crowding or niching techniques can be computationally costly or fail to compare new solutions with the most appropriate existing population member. This paper proposes a simple intervention strategy that compares each new solution with the population member it is most likely to be near, and prevents those moves that are below a threshold that decreases over the algorithm's run, allowing the algorithm to ultimately converge. Comparisons with a standard DE algorithm on a number of multi-modal problems indicate that the proposed technique can achieve real and sizable improvements.

Stochastic search techniques for multi-modal search spaces require the ability to balance explora... more Stochastic search techniques for multi-modal search spaces require the ability to balance exploration with exploitation. Exploration is required to find the best region, and exploitation is required to find the best solution (i.e. the local optimum) within this region. Compared to hill climbing which is purely exploitative, simulated annealing probabilistically allows “backward” steps which facilitate exploration. However, the balance between exploration and exploitation in simulated annealing is biased towards exploitation – improving moves are always accepted, so local (greedy) search steps can occur at even the earliest stages of the search process. The purpose of “thresheld convergence” is to have these early-stage local search steps “held” back by a threshold function. It is hypothesized that early local search steps can interfere with the effectiveness of a search technique’s (concurrent) mechanisms for global search. Experiments show that the addition of thresheld convergence to simulated annealing can lead to significant performance improvements in multi-modal search spaces.

Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one ... more Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one long-term (pheromone) and the other short-term (local heuristic). This paper details the development of antennation, a mid-term heuristic based on an analogous process in real ants. This is incorporated into ACO for the Travelling Salesman Problem (TSP). Antennation involves sharing information of the previous paths taken by ants, including information gained from previous meetings. Antennation was added to the Ant System (AS), Ant Colony System (ACS) and Ant Multi-Tour System (AMTS) algorithms. Tests were conducted on symmetric TSPs of varying size. Antennation provides an advantage when incorporated into algorithms without an inbuilt exploration mechanism and a disadvantage to those that do. AS and AMTS with antennation have superior performance when compared to their canonical form, with the effect increasing as problem size increases.

… Computation (GECCO'11), Jan 1, 2011

Differential evolution (DE) has been traditionally applied to solving benchmark continuous optimi... more Differential evolution (DE) has been traditionally applied to solving benchmark continuous optimisation functions. To enable it to solve a combinatorially oriented design problem, such as the construction of effective radio frequency identification antennas, requires the development of a suitable encoding of the discrete decision variables in a continuous space. This study introduces an encoding that allows the algorithm to construct antennas of varying complexity and length. The DE algorithm developed is a multiobjective approach that maximises antenna efficiency and minimises resonant frequency. Its results are compared with those generated by a family of ant colony optimisation (ACO) metaheuristics that have formed the standard in this area. Results indicate that DE can work well on this problem and that the proposed solution encoding is suitable. On small antenna grid sizes (hence, smaller solution spaces) DE performs well in comparison to ACO, while as the solution space increases its relative performance decreases. However, as the ACO employs a local search operator that the DE currently does not, there is scope for further improvement to the DE approach.

Evolutionary Computation (GECCO'11), Jan 1, 2011

Evolutionary Computation (GECCO'11), Jan 1, 2011

… Sixth IEEE International Conference on e …, Jan 1, 2010

… Computation (CEC), 2010 IEEE Congress on, Jan 1, 2010

Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11, 2011

Computers & Operations Research, 2008

Lecture Notes in Computer Science, 2002

Lecture Notes in Computer Science, 2004

Lecture Notes in Computer Science, 2002

Proceedings of Inaugural Workshop on Artificial Life, Adelaide, Australia, 2001

Abstract. Ant colony optimisation techniques are usually guided by pheromone and heuristic cost i... more Abstract. Ant colony optimisation techniques are usually guided by pheromone and heuristic cost information when choosing the next element to add to a solution. However, while an individual element may be attractive, usually its long term consequences are neither known nor considered. For instance, a short link in a TSP may be incorporated into an ant's solution, yet, as a consequence of this link, the rest of the path may be longer than if another link was chosen. The Accumulated Experience Ant Colony uses the previous experiences ...

International Journal of Computational Intelligence and Applications, 2003

Many algorithms have been devised for tackling combinatorial optimisation problems (COPs). Tradit... more Many algorithms have been devised for tackling combinatorial optimisation problems (COPs). Traditional Operations Research (OR) techniques such as Branch and Bound and Cutting Planes Algorithms can, given enough time, guarantee an optimal solution as they explicitly exploit features of the optimisation function they are solving. Specialised heuristics exist for most COPs that also exploit features of the optimisation function to arrive at a good, but probably not optimal, solution.

Abstract The aim of the paper is to present the results of research into component based software... more Abstract The aim of the paper is to present the results of research into component based software development by providing a specification mechanism allowing searching for components in a component repository. A new component classification framework is proposed based on which a Component Description Manager has been designed and implemented.

Each particle in a swarm maintains its current position and its personal best position. It is use... more Each particle in a swarm maintains its current position and its personal best position. It is useful to think of these personal best positions as a population of attractors – updates to current positions are based on attractions to these personal best positions. If the population of attractors has high diversity, it will encourage a broad exploration of the search space with particles being drawn in many different directions. However, the population of attractors can converge quickly – attractors can draw other particles towards them, and these particles can update their own personal bests to be near the first attractor. This convergence of attractors can be reduced by having a particle update the attractor it has approached rather than its own attractor/personal best. This simple change to the update procedure in particle swarm optimization incurs minimal computational cost, and it can lead to large performance improvements in multi-modal search spaces.

In order to relate machine learning problems we argue that we need to be able to articulate what ... more In order to relate machine learning problems we argue that we need to be able to articulate what is meant by a single machine learning problem. By attempting to name the various aspects of a learning problem we hope to clarify ways in which learning problems might be related to each other. We tentatively put forward a proposal for an anatomy of learning problems that will serve as scaffolding for posing questions about relations. After surveying the way learning problems are discussed in a range of repositories and services. We then argue that the terms used to describe problems to better understand a range of viewpoints within machine learning ranging from the theoretical to the practical.

Differential evolution (DE) is a widely-effective population-based continuous optimiser that requ... more Differential evolution (DE) is a widely-effective population-based continuous optimiser that requires convergence to automatically scale its moves. However, once its population has begun to converge its ability to conduct global search is diminished, as the difference vectors used to generate new solutions are derived from the current population members' positions. In multi-modal search spaces DE may converge too rapidly, i.e., before adequately exploring the search space to identify the best region(s) in which to conduct its finer-grained search. Traditional crowding or niching techniques can be computationally costly or fail to compare new solutions with the most appropriate existing population member. This paper proposes a simple intervention strategy that compares each new solution with the population member it is most likely to be near, and prevents those moves that are below a threshold that decreases over the algorithm's run, allowing the algorithm to ultimately converge. Comparisons with a standard DE algorithm on a number of multi-modal problems indicate that the proposed technique can achieve real and sizable improvements.

Stochastic search techniques for multi-modal search spaces require the ability to balance explora... more Stochastic search techniques for multi-modal search spaces require the ability to balance exploration with exploitation. Exploration is required to find the best region, and exploitation is required to find the best solution (i.e. the local optimum) within this region. Compared to hill climbing which is purely exploitative, simulated annealing probabilistically allows “backward” steps which facilitate exploration. However, the balance between exploration and exploitation in simulated annealing is biased towards exploitation – improving moves are always accepted, so local (greedy) search steps can occur at even the earliest stages of the search process. The purpose of “thresheld convergence” is to have these early-stage local search steps “held” back by a threshold function. It is hypothesized that early local search steps can interfere with the effectiveness of a search technique’s (concurrent) mechanisms for global search. Experiments show that the addition of thresheld convergence to simulated annealing can lead to significant performance improvements in multi-modal search spaces.

Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one ... more Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one long-term (pheromone) and the other short-term (local heuristic). This paper details the development of antennation, a mid-term heuristic based on an analogous process in real ants. This is incorporated into ACO for the Travelling Salesman Problem (TSP). Antennation involves sharing information of the previous paths taken by ants, including information gained from previous meetings. Antennation was added to the Ant System (AS), Ant Colony System (ACS) and Ant Multi-Tour System (AMTS) algorithms. Tests were conducted on symmetric TSPs of varying size. Antennation provides an advantage when incorporated into algorithms without an inbuilt exploration mechanism and a disadvantage to those that do. AS and AMTS with antennation have superior performance when compared to their canonical form, with the effect increasing as problem size increases.

… Computation (GECCO'11), Jan 1, 2011

Differential evolution (DE) has been traditionally applied to solving benchmark continuous optimi... more Differential evolution (DE) has been traditionally applied to solving benchmark continuous optimisation functions. To enable it to solve a combinatorially oriented design problem, such as the construction of effective radio frequency identification antennas, requires the development of a suitable encoding of the discrete decision variables in a continuous space. This study introduces an encoding that allows the algorithm to construct antennas of varying complexity and length. The DE algorithm developed is a multiobjective approach that maximises antenna efficiency and minimises resonant frequency. Its results are compared with those generated by a family of ant colony optimisation (ACO) metaheuristics that have formed the standard in this area. Results indicate that DE can work well on this problem and that the proposed solution encoding is suitable. On small antenna grid sizes (hence, smaller solution spaces) DE performs well in comparison to ACO, while as the solution space increases its relative performance decreases. However, as the ACO employs a local search operator that the DE currently does not, there is scope for further improvement to the DE approach.

Evolutionary Computation (GECCO'11), Jan 1, 2011

Evolutionary Computation (GECCO'11), Jan 1, 2011

… Sixth IEEE International Conference on e …, Jan 1, 2010

… Computation (CEC), 2010 IEEE Congress on, Jan 1, 2010