Articles Archive for October 2009
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Abstract This paper proposes several modifications to existing hybrid evolutionary algorithms in grid-based puzzles, using a-priori
probabilities of 0/1 occurrence in binary encodings. This calculation of a-priori probabilities of bits is possible in grid-based
problems (puzzles in this case) due to their special structure, with the solution confined into a grid. The work is focused
in two different grid-based puzzles, the Japanese puzzles and the Light-up puzzle, each one having special characteristics
in terms of constraints, which must be taken into account for the probabilities of bit calculation. For these puzzles, we
show the process of a-priori probabilities calculation, and we modify the initialization of the EAs to improve their performance.
We also include novel mutation operators based on a-priori probabilities, which makes more effective the evolutionary search
of the algorithms in the tackled puzzles. The performance of the algorithms with these new initialization and novel mutation
operators is compared with the performance without them. We show that the new initialization and operators based on a-priori
probabilities of bits make the evolutionary search more effective and also improve the scalability of the algorithms.
probabilities of 0/1 occurrence in binary encodings. This calculation of a-priori probabilities of bits is possible in grid-based
problems (puzzles in this case) due to their special structure, with the solution confined into a grid. The work is focused
in two different grid-based puzzles, the Japanese puzzles and the Light-up puzzle, each one having special characteristics
in terms of constraints, which must be taken into account for the probabilities of bit calculation. For these puzzles, we
show the process of a-priori probabilities calculation, and we modify the initialization of the EAs to improve their performance.
We also include novel mutation operators based on a-priori probabilities, which makes more effective the evolutionary search
of the algorithms in the tackled puzzles. The performance of the algorithms with these new initialization and novel mutation
operators is compared with the performance without them. We show that the new initialization and operators based on a-priori
probabilities of bits make the evolutionary search more effective and also improve the scalability of the algorithms.
- Content Type Journal Article
- Category Special Issue
- DOI 10.1007/s12065-009-0030-3
- Authors
- E. G. Ortiz-García, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
- S. Salcedo-Sanz, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
- Á. M. Pérez-Bellido, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
- L. Carro-Calvo, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
- A. Portilla-Figueras, Universidad de Alcalá, Escuela Politécnica Superior Department of Signal Theory and Communications Alcalá de Henares 28871 Madrid Spain
- X. Yao, The University of Birmingham The Centre for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science Birmingham UK
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Number 4 / December, 2009
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Abstract An evolutionary synthesis method to generate digital filters with low coefficient sensitivity is presented. The method uses
a chromosome coding based on the graph adjacency matrix representation. It is shown that the proposed chromosome representation
enables to easily verify and avoid the generation of topologically invalid and non-computable individuals during the evolutionary
process. The efficiency of the proposed algorithm is tested in the synthesis of two low-pass digital filters and the results
are compared with other examples found in the literature.
a chromosome coding based on the graph adjacency matrix representation. It is shown that the proposed chromosome representation
enables to easily verify and avoid the generation of topologically invalid and non-computable individuals during the evolutionary
process. The efficiency of the proposed algorithm is tested in the synthesis of two low-pass digital filters and the results
are compared with other examples found in the literature.
- Content Type Journal Article
- Category Research Paper
- DOI 10.1007/s12065-009-0028-x
- Authors
- Leonardo Bruno de Sá, Brazilian Army Technological Center Av das Américas, 28705, Guaratiba Rio de Janeiro 23020-470 Brazil
- Antonio Mesquita, Federal University of Rio de Janeiro Rio de Janeiro Brazil
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Number 3 / December, 2009
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Abstract In this paper, we present an evolutionary multi-objective learning model achieving cooperation between the rule base and the
adaptive fuzzy operators of the inference system in order to obtain simpler, more compact and still accurate linguistic fuzzy
models by learning fuzzy inference adaptive operators together with rules. The multi-objective evolutionary algorithm proposed
generates a set of fuzzy rule based systems with different trade-offs between interpretability and accuracy, allowing the
designers to select the one that involves the most suitable balance for the desired application. We develop an experimental
study testing our approach with some variants on nine real-world regression datasets finding the advantages of cooperative
compared to sequential models, as well as multi-objective compared with single-objective models. The study is elaborated comparing
different approaches by applying non-parametric statistical tests for pair-wise. Results confirm the usefulness of the proposed
approach.
adaptive fuzzy operators of the inference system in order to obtain simpler, more compact and still accurate linguistic fuzzy
models by learning fuzzy inference adaptive operators together with rules. The multi-objective evolutionary algorithm proposed
generates a set of fuzzy rule based systems with different trade-offs between interpretability and accuracy, allowing the
designers to select the one that involves the most suitable balance for the desired application. We develop an experimental
study testing our approach with some variants on nine real-world regression datasets finding the advantages of cooperative
compared to sequential models, as well as multi-objective compared with single-objective models. The study is elaborated comparing
different approaches by applying non-parametric statistical tests for pair-wise. Results confirm the usefulness of the proposed
approach.
- Content Type Journal Article
- Category Special Issue
- DOI 10.1007/s12065-009-0026-z
- Authors
- Antonio A. Márquez, University of Huelva Information Technologies Department Huelva Spain
- Francisco A. Márquez, University of Huelva Information Technologies Department Huelva Spain
- Antonio Peregrín, University of Huelva Information Technologies Department Huelva Spain
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Numbers 1-2 / November, 2009
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Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets
Abstract Exploiting the information in low quality datasets has been recently acknowledged as a new challenge in Genetic Fuzzy Systems.
Owing to this, in this paper we discuss the basic principles that govern the extension of a fuzzy rule based classifier to
interval and fuzzy data. We have also applied these principles to the genetic learning of a simple cooperative-competitive
algorithm, that becomes the first example of a Genetic Fuzzy Classifier able to use low quality data. Additionally, we introduce
a benchmark, comprising some synthetic samples and two real-world problems that involve interval and fuzzy-valued data, that
can be used to assess future algorithms of the same kind.
Owing to this, in this paper we discuss the basic principles that govern the extension of a fuzzy rule based classifier to
interval and fuzzy data. We have also applied these principles to the genetic learning of a simple cooperative-competitive
algorithm, that becomes the first example of a Genetic Fuzzy Classifier able to use low quality data. Additionally, we introduce
a benchmark, comprising some synthetic samples and two real-world problems that involve interval and fuzzy-valued data, that
can be used to assess future algorithms of the same kind.
- Content Type Journal Article
- Category Special Issue
- DOI 10.1007/s12065-009-0024-1
- Authors
- Ana M. Palacios, Universidad de Oviedo Departamento de Informática 33071 Gijón Asturias Spain
- Luciano Sánchez, Universidad de Oviedo Departamento de Informática 33071 Gijón Asturias Spain
- Inés Couso, Universidad de Oviedo Departamento de Estadística e I.O. y D.M 33071 Gijón Asturias Spain
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Numbers 1-2 / November, 2009
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Abstract The purpose of this work is to automatically design vision algorithms for a mobile robot, adapted to its current visual context.
In this paper we address the particular task of obstacle avoidance using monocular vision. Starting from a set of primitives
composed of the different techniques found in the literature, we propose a generic structure to represent the algorithms,
using standard resolution video sequences as an input, and velocity commands to control a wheel robot as an output. Grammar
rules are then used to construct correct instances of algorithms, that are then evaluated using different protocols: evaluation
of trajectories performed in a goal reaching task, or imitation of a hand-guided trajectory. A genetic program is applied
to evolve populations of algorithms in order to optimize the performances of the controllers. The first results obtained in
a simulated environment show that the evolution produces algorithms that can be easily interpreted and which are clearly adapted
to the visual context. However, the resulting trajectories are often erratic, and the generalization capacities are poor.
To improve the results, we propose to use a two-phase evolution combining imitation and goal reaching evaluations, and to
add some constraints in the grammar rules to enforce a more generic behavior. The results obtained in simulation show that
the evolved algorithms are more efficient and more generic. Finally, we apply the imitation based evolution on real sequences
and test the evolved algorithms on a real robot. Though simplified by dropping the goal reaching constraint, the resulting
algorithms behave well in a corridor centering task, and show certain generalization capacities.
In this paper we address the particular task of obstacle avoidance using monocular vision. Starting from a set of primitives
composed of the different techniques found in the literature, we propose a generic structure to represent the algorithms,
using standard resolution video sequences as an input, and velocity commands to control a wheel robot as an output. Grammar
rules are then used to construct correct instances of algorithms, that are then evaluated using different protocols: evaluation
of trajectories performed in a goal reaching task, or imitation of a hand-guided trajectory. A genetic program is applied
to evolve populations of algorithms in order to optimize the performances of the controllers. The first results obtained in
a simulated environment show that the evolution produces algorithms that can be easily interpreted and which are clearly adapted
to the visual context. However, the resulting trajectories are often erratic, and the generalization capacities are poor.
To improve the results, we propose to use a two-phase evolution combining imitation and goal reaching evaluations, and to
add some constraints in the grammar rules to enforce a more generic behavior. The results obtained in simulation show that
the evolved algorithms are more efficient and more generic. Finally, we apply the imitation based evolution on real sequences
and test the evolved algorithms on a real robot. Though simplified by dropping the goal reaching constraint, the resulting
algorithms behave well in a corridor centering task, and show certain generalization capacities.
- Content Type Journal Article
- Category Research Paper
- DOI 10.1007/s12065-009-0021-4
- Authors
- Renaud Barate, ENSTA 32 Bd Victor 75739 Paris Cedex 15 France
- Antoine Manzanera, ENSTA 32 Bd Victor 75739 Paris Cedex 15 France
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Number 3 / December, 2009
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Introduction: special issue on parallel and distributed evolutionary algorithms, part I
- Content Type Journal Article
- Category Editorial
- DOI 10.1007/s10710-009-9094-1
- Authors
- Marco Tomassini, University of Lausanne Information Systems Department, HEC Lausanne Switzerland
- Leonardo Vanneschi, University of Milano-Bicocca Department of Informatics, Systems and Communication (D.I.S.Co.) Milan Italy
- Journal Genetic Programming and Evolvable Machines
- Online ISSN 1573-7632
- Print ISSN 1389-2576
- Journal Volume Volume 10
- Journal Issue Volume 10, Number 4 / December, 2009
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Special issue on genetic fuzzy systems: new advances
- Content Type Journal Article
- Category Editorial
- DOI 10.1007/s12065-009-0027-y
- Authors
- Rafael Alcalá, University of Granada Department of Computer Science and A.I. 18071 Granada Spain
- Yusuke Nojima, Osaka Prefecture University Department of Computer Science and Intelligent Systems 1-1 Gakuen-cho, Naka-ku Sakai, Osaka 599-8531 Japan
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Numbers 1-2 / November, 2009
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Abstract This paper suggests a genetic fuzzy system approach to develop bidding strategies for agents in online auction environments.
Assessing efficient bidding strategies is a key to evaluate auction models and verify if the underlying mechanism design achieves
its intended goals. Due to its relevance in current energy markets worldwide, we use day-ahead electricity auctions as an
experimental and application instance of the approach developed in this paper. Successful fuzzy bidding strategies have been
developed by genetic fuzzy systems using coevolutionary algorithms. In this paper we address a coevolutionary fuzzy system
algorithm and present recent results concerning bidding strategies behavior. Coevolutionary approaches developed by coevolutionary
agents interact through their fuzzy bidding strategies in a multiagent environment and allow realistic and transparent representations
of agents behavior in auction-based markets. They also improve market representation and evaluation mechanisms. In particular,
we study how the coevolutionary fuzzy bidding strategies perform against each other during hourly electric energy auctions.
Experimental results show that coevolutionary agents may enhance their profits at the cost of increasing system hourly price
paid by demand.
Assessing efficient bidding strategies is a key to evaluate auction models and verify if the underlying mechanism design achieves
its intended goals. Due to its relevance in current energy markets worldwide, we use day-ahead electricity auctions as an
experimental and application instance of the approach developed in this paper. Successful fuzzy bidding strategies have been
developed by genetic fuzzy systems using coevolutionary algorithms. In this paper we address a coevolutionary fuzzy system
algorithm and present recent results concerning bidding strategies behavior. Coevolutionary approaches developed by coevolutionary
agents interact through their fuzzy bidding strategies in a multiagent environment and allow realistic and transparent representations
of agents behavior in auction-based markets. They also improve market representation and evaluation mechanisms. In particular,
we study how the coevolutionary fuzzy bidding strategies perform against each other during hourly electric energy auctions.
Experimental results show that coevolutionary agents may enhance their profits at the cost of increasing system hourly price
paid by demand.
- Content Type Journal Article
- Category Special Issue
- DOI 10.1007/s12065-009-0023-2
- Authors
- Igor Walter, Brazilian Electricity Regulatory Agency, ANEEL Brasília DF Brazil
- Fernando Gomide, University of Campinas, Unicamp Faculty of Electrical and Computer Engineering, FEEC Campinas SP Brazil
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Numbers 1-2 / November, 2009
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Abstract The tuning of Fuzzy Rule-Based Systems is often applied to improve their performance as a post-processing stage once an initial
set of fuzzy rules has been extracted. This optimization problem can become a hard one when the size of the considered system
in terms of the number of variables, rules and, particularly, data samples is big. Distributed Genetic Algorithms are excellent
optimization algorithms which exploit the nowadays available parallel hardware (multicore microprocessors and clusters) and
could help to alleviate this growth in complexity. In this work, we present a study on the use of the Distributed Genetic
Algorithms for the tuning of Fuzzy Rule-Based Systems. To this end, we analyze the application of a specific Gradual Distributed
Real-Coded Genetic Algorithm which employs eight subpopulations in a hypercube topology and local parallelization at each
subpopulation. We tested our approach on nine real-world datasets of different sizes and with different numbers of variables.
The empirical performance in solution quality and computing time is assessed by comparing its results with those from a highly
effective sequential tuning algorithm. The results show that the distributed approach achieves better results in terms of
quality and execution time as the complexity of the problem grows.
set of fuzzy rules has been extracted. This optimization problem can become a hard one when the size of the considered system
in terms of the number of variables, rules and, particularly, data samples is big. Distributed Genetic Algorithms are excellent
optimization algorithms which exploit the nowadays available parallel hardware (multicore microprocessors and clusters) and
could help to alleviate this growth in complexity. In this work, we present a study on the use of the Distributed Genetic
Algorithms for the tuning of Fuzzy Rule-Based Systems. To this end, we analyze the application of a specific Gradual Distributed
Real-Coded Genetic Algorithm which employs eight subpopulations in a hypercube topology and local parallelization at each
subpopulation. We tested our approach on nine real-world datasets of different sizes and with different numbers of variables.
The empirical performance in solution quality and computing time is assessed by comparing its results with those from a highly
effective sequential tuning algorithm. The results show that the distributed approach achieves better results in terms of
quality and execution time as the complexity of the problem grows.
- Content Type Journal Article
- Category Special Issue
- DOI 10.1007/s12065-009-0025-0
- Authors
- I. Robles, University of Granada Dept. of Computer Sciences and Artificial Intelligence 18071 Granada Spain
- R. Alcalá, University of Granada Dept. of Computer Sciences and Artificial Intelligence 18071 Granada Spain
- J. M. Benítez, University of Granada Dept. of Computer Sciences and Artificial Intelligence 18071 Granada Spain
- F. Herrera, University of Granada Dept. of Computer Sciences and Artificial Intelligence 18071 Granada Spain
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Numbers 1-2 / November, 2009
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Abstract In this paper, we propose a multi-objective evolutionary algorithm (MOEA) to generate Mamdani fuzzy rule-based systems with
different trade-offs between accuracy and complexity by learning concurrently granularities of the input and output partitions,
membership function (MF) parameters and rules. To this aim, we introduce the concept of virtual and concrete partitions: the
former is defined by uniformly partitioning each linguistic variable with a fixed maximum number of fuzzy sets; the latter
takes into account, for each variable, the number of fuzzy sets determined by the evolutionary process. Rule bases and MF
parameters are defined on the virtual partitions and, whenever a fitness evaluation is required, mapped to the concrete partitions
by employing appropriate mapping strategies. The implementation of the MOEA relies on a chromosome composed of three parts,
which codify the partition granularities, the virtual rule base and the membership function parameters, respectively, and
on purposely-defined genetic operators. The MOEA has been tested on three real-world regression problems achieving very promising
results. In particular, we highlight how starting from randomly generated solutions, the MOEA is able to determine different
granularities for different variables achieving good trade-offs between complexity and accuracy.
different trade-offs between accuracy and complexity by learning concurrently granularities of the input and output partitions,
membership function (MF) parameters and rules. To this aim, we introduce the concept of virtual and concrete partitions: the
former is defined by uniformly partitioning each linguistic variable with a fixed maximum number of fuzzy sets; the latter
takes into account, for each variable, the number of fuzzy sets determined by the evolutionary process. Rule bases and MF
parameters are defined on the virtual partitions and, whenever a fitness evaluation is required, mapped to the concrete partitions
by employing appropriate mapping strategies. The implementation of the MOEA relies on a chromosome composed of three parts,
which codify the partition granularities, the virtual rule base and the membership function parameters, respectively, and
on purposely-defined genetic operators. The MOEA has been tested on three real-world regression problems achieving very promising
results. In particular, we highlight how starting from randomly generated solutions, the MOEA is able to determine different
granularities for different variables achieving good trade-offs between complexity and accuracy.
- Content Type Journal Article
- Category Special Issue
- DOI 10.1007/s12065-009-0022-3
- Authors
- Michela Antonelli, University of Pisa Dipartimento di Ingegneria dell’Informazione: Elettronica, Informatica, Telecomunicazioni Via Diotisalvi 2 56122 Pisa Italy
- Pietro Ducange, University of Pisa Dipartimento di Ingegneria dell’Informazione: Elettronica, Informatica, Telecomunicazioni Via Diotisalvi 2 56122 Pisa Italy
- Beatrice Lazzerini, University of Pisa Dipartimento di Ingegneria dell’Informazione: Elettronica, Informatica, Telecomunicazioni Via Diotisalvi 2 56122 Pisa Italy
- Francesco Marcelloni, University of Pisa Dipartimento di Ingegneria dell’Informazione: Elettronica, Informatica, Telecomunicazioni Via Diotisalvi 2 56122 Pisa Italy
- Journal Evolutionary Intelligence
- Online ISSN 1864-5917
- Print ISSN 1864-5909
- Journal Volume Volume 2
- Journal Issue Volume 2, Numbers 1-2 / November, 2009
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Abstract General Purpose computing over Graphical Processing Units (GPGPUs) is a huge shift of paradigm in parallel computing that
promises a dramatic increase in performance. But GPGPUs also bring an unprecedented level of complexity in algorithmic design
and software development. In this paper we describe the challenges and design choices involved in parallelizing a hybrid of
Genetic Algorithm (GA) and Local Search (LS) to solve MAXimum SATisfiability (MAX-SAT) problem on a state-of-the-art nVidia
Tesla GPU using nVidia Compute Unified Device Architecture (CUDA). MAX-SAT is a problem of practical importance and is often
solved by employing metaheuristics based search methods like GAs and hybrid of GA with LS. Almost all the parallel GAs (pGAs)
designed in the last two decades were designed for either clusters or MPPs. Unfortunately, very little research is done on
the implementation of such algorithms over commodity graphics hardware. GAs in their simple form are not suitable for implementation
over the Single Instruction Multiple Thread (SIMT) architecture of a GPU, and the same is the case with conventional LS algorithms.
In this paper we explore different genetic operators that can be used for an efficient implementation of GAs over nVidia GPUs.
We also design and introduce new techniques/operators for an efficient implementation of GAs and LS over such architectures.
We use nVidia Tesla C1060 to perform several numerical tests and performance measurements and show that in the best case we
obtain a speedup of 25×. We also discuss the effects of different optimization techniques on the overall execution time.
promises a dramatic increase in performance. But GPGPUs also bring an unprecedented level of complexity in algorithmic design
and software development. In this paper we describe the challenges and design choices involved in parallelizing a hybrid of
Genetic Algorithm (GA) and Local Search (LS) to solve MAXimum SATisfiability (MAX-SAT) problem on a state-of-the-art nVidia
Tesla GPU using nVidia Compute Unified Device Architecture (CUDA). MAX-SAT is a problem of practical importance and is often
solved by employing metaheuristics based search methods like GAs and hybrid of GA with LS. Almost all the parallel GAs (pGAs)
designed in the last two decades were designed for either clusters or MPPs. Unfortunately, very little research is done on
the implementation of such algorithms over commodity graphics hardware. GAs in their simple form are not suitable for implementation
over the Single Instruction Multiple Thread (SIMT) architecture of a GPU, and the same is the case with conventional LS algorithms.
In this paper we explore different genetic operators that can be used for an efficient implementation of GAs over nVidia GPUs.
We also design and introduce new techniques/operators for an efficient implementation of GAs and LS over such architectures.
We use nVidia Tesla C1060 to perform several numerical tests and performance measurements and show that in the best case we
obtain a speedup of 25×. We also discuss the effects of different optimization techniques on the overall execution time.
- Content Type Journal Article
- Category Original Paper
- DOI 10.1007/s10710-009-9091-4
- Authors
- Asim Munawar, Hokkaido University Graduate School of Information Science & Technology Sapporo Japan
- Mohamed Wahib, Hokkaido University Graduate School of Information Science & Technology Sapporo Japan
- Masaharu Munetomo, Hokkaido University Information Initiative Center Sapporo Japan
- Kiyoshi Akama, Hokkaido University Information Initiative Center Sapporo Japan
- Journal Genetic Programming and Evolvable Machines
- Online ISSN 1573-7632
- Print ISSN 1389-2576
- Journal Volume Volume 10
- Journal Issue Volume 10, Number 4 / December, 2009
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Abstract Parallel and distributed methods for evolutionary algorithms have concentrated on maintaining multiple populations of genotypes,
where each genotype in a population encodes a potential solution to the problem. In this paper, we investigate the parallelisation
of the genotype itself into a collection of independent chromosomes which can be evaluated in parallel. We call this multi-chromosomal evolution
(MCE). We test this approach using Cartesian Genetic Programming and apply MCE to a series of digital circuit design problems
to compare the efficacy of MCE with a conventional single chromosome approach (SCE). MCE can be readily used for many digital
circuits because they have multiple outputs. In MCE, an independent chromosome is assigned to each output. When we compare
MCE with SCE we find that MCE allows us to evolve solutions much faster. In addition, in some cases we were able to evolve
solutions with MCE that we unable to with SCE. In a case-study, we investigate how MCE can be applied to to a single objective
problem in the domain of image classification, namely, the classification of breast X-rays for cancer. To apply MCE to this
problem, we identify regions of interest (RoI) from the mammograms, divide the RoI into a collection of sub-images and use
a chromosome to classify each sub-image. This problem allows us to evaluate various evolutionary mutation operators which
can pairwise swap chromosomes either randomly or topographically or reuse chromosomes in place of other chromosomes.
where each genotype in a population encodes a potential solution to the problem. In this paper, we investigate the parallelisation
of the genotype itself into a collection of independent chromosomes which can be evaluated in parallel. We call this multi-chromosomal evolution
(MCE). We test this approach using Cartesian Genetic Programming and apply MCE to a series of digital circuit design problems
to compare the efficacy of MCE with a conventional single chromosome approach (SCE). MCE can be readily used for many digital
circuits because they have multiple outputs. In MCE, an independent chromosome is assigned to each output. When we compare
MCE with SCE we find that MCE allows us to evolve solutions much faster. In addition, in some cases we were able to evolve
solutions with MCE that we unable to with SCE. In a case-study, we investigate how MCE can be applied to to a single objective
problem in the domain of image classification, namely, the classification of breast X-rays for cancer. To apply MCE to this
problem, we identify regions of interest (RoI) from the mammograms, divide the RoI into a collection of sub-images and use
a chromosome to classify each sub-image. This problem allows us to evaluate various evolutionary mutation operators which
can pairwise swap chromosomes either randomly or topographically or reuse chromosomes in place of other chromosomes.
- Content Type Journal Article
- Category Original Paper
- DOI 10.1007/s10710-009-9093-2
- Authors
- James Alfred Walker, University of York Intelligent Systems Group, Department of Electronics Heslington York YO10 5DD UK
- Katharina Völk, University of York Intelligent Systems Group, Department of Electronics Heslington York YO10 5DD UK
- Stephen L. Smith, University of York Intelligent Systems Group, Department of Electronics Heslington York YO10 5DD UK
- Julian Francis Miller, University of York Intelligent Systems Group, Department of Electronics Heslington York YO10 5DD UK
- Journal Genetic Programming and Evolvable Machines
- Online ISSN 1573-7632
- Print ISSN 1389-2576
- Journal Volume Volume 10
- Journal Issue Volume 10, Number 4 / December, 2009
