International Journal of Bio-Inspired Computation http://www.inderscience.com/browse/index.php?journalID=329&year=2012&vol=4&issue=1 Inderscience Publishers Ltd en-uk International Journal of Bio-Inspired Computation 1758-0366 1758-0374 © 2012 Inderscience Publishers Ltd editor@inderscience.com https://www.inderscience.com/images/files/coverImgs/ijbic_scoverijbic.jpg http://www.inderscience.com/browse/index.php?journalID=329&year=2012&vol=4&issue=1 http://www.inderscience.com/link.php?id=44932 Efficiency of an optimisation process is largely determined by the search algorithm and its fundamental characteristics. In a given optimisation, a single type of algorithm is used in most applications. In this paper, we will investigate the eagle strategy recently developed for global optimisation, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimisation problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to ten in many applications. Two-stage eagle strategy with differential evolution
Xin-She Yang; Suash Deb
International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 1 - 5
Efficiency of an optimisation process is largely determined by the search algorithm and its fundamental characteristics. In a given optimisation, a single type of algorithm is used in most applications. In this paper, we will investigate the eagle strategy recently developed for global optimisation, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimisation problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to ten in many applications.

]]> 10.1504/IJBIC.2012.044932 International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 1 - 5 Xin-She Yang; Suash Deb Mathematics and Scientific Computing, National Physical Laboratory, Teddington TW11 0LW, UK. ' Department of Computer Science and Engineering, C.V. Raman College of Engineering, Bidyanagar, Mahura, Janla, Bhubaneswar 752054, India bat algorithm cuckoo search eagle strategy bio-inspired computation differential evolution global optimisation pressure vessel design speed reducer design. 2012-01-16T23:20:50-05:00 4 1 1 5 2012-01-16T23:20:50-05:00 http://www.inderscience.com/link.php?id=44931 Usual visualisation techniques for multidimensional datasets, such as parallel coordinates and scatterplot matrices, do not scale well to high numbers of dimensions. A common approach to solve this problem is dimensionality selection. Existing dimensionality selection techniques usually select pertinent dimension subsets that are significant to the user without loose of information. We present concrete cooperation between automatic algorithms, interactive algorithms and visualisation tools: the evolutionary algorithm is used to obtain optimal dimension subsets which represent the original dataset without loosing information for unsupervised mode (clustering or outlier detection). The last effective cooperation is a visualisation tool used to present the user interactive evolutionary algorithm results and let him actively participate in evolutionary algorithm searching with more efficiency resulting in a faster evolutionary algorithm convergence. We have implemented our approach and applied it to real dataset to confirm this approach is effective for supporting the user in the exploration of high dimensional datasets. Visual interactive evolutionary algorithm for high dimensional outlier detection and data clustering problems
Lydia Boudjeloud-Assala
International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 6 - 13
Usual visualisation techniques for multidimensional datasets, such as parallel coordinates and scatterplot matrices, do not scale well to high numbers of dimensions. A common approach to solve this problem is dimensionality selection. Existing dimensionality selection techniques usually select pertinent dimension subsets that are significant to the user without loose of information. We present concrete cooperation between automatic algorithms, interactive algorithms and visualisation tools: the evolutionary algorithm is used to obtain optimal dimension subsets which represent the original dataset without loosing information for unsupervised mode (clustering or outlier detection). The last effective cooperation is a visualisation tool used to present the user interactive evolutionary algorithm results and let him actively participate in evolutionary algorithm searching with more efficiency resulting in a faster evolutionary algorithm convergence. We have implemented our approach and applied it to real dataset to confirm this approach is effective for supporting the user in the exploration of high dimensional datasets.

]]> 10.1504/IJBIC.2012.044931 International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 6 - 13 Lydia Boudjeloud-Assala LITA, Université Paul Verlaine – Metz, Ile du Saulcy, 57045 Metz Cedex 01, France evolutionary algorithms EAs visual interactive algorithms high dimensionality data clustering outlier detection bio-inspired computation visualisation automatic algorithms high dimensional datasets. 2012-01-16T23:20:50-05:00 4 1 6 13 2012-01-16T23:20:50-05:00 http://www.inderscience.com/link.php?id=44927 Evolutionary learning of neural networks, i.e., neuroevolution, has shown to play an important role in agent constitutions. It has the robustness property for dynamic, practical problems. In the case of a large number of input neurons, however, the search space of neuroevolution becomes much larger so that it is difficult to find out better policies. In this paper, Isomap, one of the manifold learning algorithms, is employed to reduce the dimensionality of the input space. The Isomap tries to reduce the dimensionality based on manifold structures in high dimensional space and to preserve local topological relationships among data. Mario AI is used as a test bed for the proposed method. Video games such as Mario, Ms. Pac-Man, and car racing have been recognised as ideal benchmark problems for computational intelligence, where they require a variety of inputs, real-time strategy, and so on, and they provide good simulators which are capable to apply CI techniques. A large number of scenes in Mario are applied by the Isomap in order to constitute a map from scene information to low dimensional data. Experimental results on the Mario AI show the effectiveness of the proposed method. Neuroevolution with manifold learning for playing Mario
H. Handa
International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 14 - 26
Evolutionary learning of neural networks, i.e., neuroevolution, has shown to play an important role in agent constitutions. It has the robustness property for dynamic, practical problems. In the case of a large number of input neurons, however, the search space of neuroevolution becomes much larger so that it is difficult to find out better policies. In this paper, Isomap, one of the manifold learning algorithms, is employed to reduce the dimensionality of the input space. The Isomap tries to reduce the dimensionality based on manifold structures in high dimensional space and to preserve local topological relationships among data. Mario AI is used as a test bed for the proposed method. Video games such as Mario, Ms. Pac-Man, and car racing have been recognised as ideal benchmark problems for computational intelligence, where they require a variety of inputs, real-time strategy, and so on, and they provide good simulators which are capable to apply CI techniques. A large number of scenes in Mario are applied by the Isomap in order to constitute a map from scene information to low dimensional data. Experimental results on the Mario AI show the effectiveness of the proposed method.

]]> 10.1504/IJBIC.2012.044927 International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 14 - 26 H. Handa Okayama University, Tsushima-Naka 3-1-1, Okayama, Japan neuroevolution manifold learning particle swarm optimisation PSO Mario Isomap evolutionary learning neural networks video games computational intelligence. 2012-01-16T23:20:50-05:00 4 1 14 26 2012-01-16T23:20:50-05:00 http://www.inderscience.com/link.php?id=44935 Choice design building based on D-error minimisation can be accomplished either by using predefined β values or by assuming probabilistic distributions for them. Several mathematical techniques have been used for both approaches in the past, resulting in algorithms that obtain efficient designs, which guarantee the high quality of the information that will be provided by the respondents. This paper proposes the use of a genetic algorithm for dealing with the problem of building designs with minimum D-error, describing the technique and applying it successfully to several benchmark cases. Design matrices, D-error values, percentages of level overlap and computation times are provided for each case. Use of a genetic algorithm for building efficient choice designs
Jesús Muñuzuri; Pablo Cortés Achedad; María Rodríguez; Rafael Grosso
International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 27 - 32
Choice design building based on D-error minimisation can be accomplished either by using predefined β values or by assuming probabilistic distributions for them. Several mathematical techniques have been used for both approaches in the past, resulting in algorithms that obtain efficient designs, which guarantee the high quality of the information that will be provided by the respondents. This paper proposes the use of a genetic algorithm for dealing with the problem of building designs with minimum D-error, describing the technique and applying it successfully to several benchmark cases. Design matrices, D-error values, percentages of level overlap and computation times are provided for each case.

]]> 10.1504/IJBIC.2012.044935 International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 27 - 32 Jesús Muñuzuri; Pablo Cortés Achedad; María Rodríguez; Rafael Grosso School of Engineering, University of Seville, Camino de los Descubrimientos, s/n 41092, Seville, Spain. ' School of Engineering, University of Seville, Camino de los Descubrimientos, s/n 41092, Seville, Spain. ' School of Engineering, University of Seville, Camino de los Descubrimientos, s/n 41092, Seville, Spain. ' School of Engineering, University of Seville, Camino de los Descubrimientos, s/n 41092, Seville, Spain discrete choice design building D-error minimisation genetic algorithms GAs choice designs. 2012-01-16T23:20:50-05:00 4 1 27 32 2012-01-16T23:20:50-05:00 http://www.inderscience.com/link.php?id=44934 Gravitational search algorithm (GSA) is one of the new optimisation algorithms based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a novel GSA with wavelet mutation (WM) (GSAWM) is proposed. It utilises the wavelet theory to enhance the GSA in exploring the solution space more effectively for a better solution. This algorithm is utilised for the optimal solutions of different economic load dispatch (ELD) problems of power systems. The obtained results are compared with those of the other state-of-the-art heuristic optimisation techniques published in the literature. Both the near-optimality of the solution and the convergence speed of the algorithm are promising. A maiden application of gravitational search algorithm with wavelet mutation for the solution of economic load dispatch problems
A. Chatterjee; S.P. Ghoshal; V. Mukherjee
International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 33 - 46
Gravitational search algorithm (GSA) is one of the new optimisation algorithms based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a novel GSA with wavelet mutation (WM) (GSAWM) is proposed. It utilises the wavelet theory to enhance the GSA in exploring the solution space more effectively for a better solution. This algorithm is utilised for the optimal solutions of different economic load dispatch (ELD) problems of power systems. The obtained results are compared with those of the other state-of-the-art heuristic optimisation techniques published in the literature. Both the near-optimality of the solution and the convergence speed of the algorithm are promising.

]]> 10.1504/IJBIC.2012.044934 International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 33 - 46 A. Chatterjee; S.P. Ghoshal; V. Mukherjee Department of Electrical Engineering, Asansol Engineering College, Asansol, West Bengal, India. ' Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal, India. ' Department of Electrical Engineering, Indian School of Mines, Dhanbad, Jharkhand, India economic load dispatch ELD gravitational search algorithm GSA prohibited operating zones ramp rate limits valve point effects wavelet mutation power systems. 2012-01-16T23:20:50-05:00 4 1 33 46 2012-01-16T23:20:50-05:00 http://www.inderscience.com/link.php?id=44930 This article demonstrates a fuzzy goal programming (FGP) approach with the use of genetic algorithm (GA) for proper deployment of patrol manpower to various road-segment areas in urban environment in different shifts of a time period to deterring violation of traffic rules and thereby reducing the accident rates in a traffic control planning horizon. To expound the potential use of the approach, a case example of the city Kolkata, West Bengal, INDIA, is solved. An application of genetic algorithm method for solving patrol manpower deployment problems through fuzzy goal programming in traffic management system: a case study
Bijay Baran Pal; Debjani Chakraborti; Papun Biswas; Anirban Mukhopadhyay
International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 47 - 60
This article demonstrates a fuzzy goal programming (FGP) approach with the use of genetic algorithm (GA) for proper deployment of patrol manpower to various road-segment areas in urban environment in different shifts of a time period to deterring violation of traffic rules and thereby reducing the accident rates in a traffic control planning horizon. To expound the potential use of the approach, a case example of the city Kolkata, West Bengal, INDIA, is solved.

]]> 10.1504/IJBIC.2012.044930 International Journal of Bio-Inspired Computation, Vol. 4, No. 1 (2012) pp. 47 - 60 Bijay Baran Pal; Debjani Chakraborti; Papun Biswas; Anirban Mukhopadhyay Department of Mathematics, University of Kalyani, Kalyani-741235, West Bengal, India. ' Department of Mathematics, Narula Institute of Technology, Kolkata-700109, West Bengal, India. ' Department of Electrical Engineering, JIS College of Engineering, Kalyani-741235, West Bengal, India. ' Department of Computer Science and Engineering, University of Kalyani, Kalyani-741235, West Bengal, India fuzzy goal programming FGP genetic algorithms GAs membership functions traffic management traffic patrols manpower deployment patrol manpower urban environments shifts accident rates traffic control planning India traffic rules. 2012-01-16T23:20:50-05:00 4 1 47 60 2012-01-16T23:20:50-05:00