The Experts below are selected from a list of 8838 Experts worldwide ranked by ideXlab platform
T Nakamura - One of the best experts on this subject based on the ideXlab platform.
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multi Start Node genetic network programming for controlling multiple agents
Systems Man and Cybernetics, 2006Co-Authors: Tadahiko Murata, T NakamuraAbstract:In this paper, we propose a variant of Genetic Network Programming (GNP) which has multiple Start Nodes. GNP is a kind of evolutionary methods inspired from Genetic Programming (GP). While GP has tree architecture, GNP has a network architecture by which a single agent is controlled. In this paper, by increasing the number of Start Nodes in a network of GNP, we extend GNP to control multiple agents. We applied the extended GNP called Multi-Start Node GNP (MS-GNP) to a load transportation problem and a tile world problem.
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SMC - Multi-Start Node Genetic Network Programming for Controlling Multiple Agents
2006 IEEE International Conference on Systems Man and Cybernetics, 2006Co-Authors: Tadahiko Murata, T NakamuraAbstract:In this paper, we propose a variant of Genetic Network Programming (GNP) which has multiple Start Nodes. GNP is a kind of evolutionary methods inspired from Genetic Programming (GP). While GP has tree architecture, GNP has a network architecture by which a single agent is controlled. In this paper, by increasing the number of Start Nodes in a network of GNP, we extend GNP to control multiple agents. We applied the extended GNP called Multi-Start Node GNP (MS-GNP) to a load transportation problem and a tile world problem.
Tadahiko Murata - One of the best experts on this subject based on the ideXlab platform.
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multi Start Node genetic network programming for controlling multiple agents
Systems Man and Cybernetics, 2006Co-Authors: Tadahiko Murata, T NakamuraAbstract:In this paper, we propose a variant of Genetic Network Programming (GNP) which has multiple Start Nodes. GNP is a kind of evolutionary methods inspired from Genetic Programming (GP). While GP has tree architecture, GNP has a network architecture by which a single agent is controlled. In this paper, by increasing the number of Start Nodes in a network of GNP, we extend GNP to control multiple agents. We applied the extended GNP called Multi-Start Node GNP (MS-GNP) to a load transportation problem and a tile world problem.
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SMC - Multi-Start Node Genetic Network Programming for Controlling Multiple Agents
2006 IEEE International Conference on Systems Man and Cybernetics, 2006Co-Authors: Tadahiko Murata, T NakamuraAbstract:In this paper, we propose a variant of Genetic Network Programming (GNP) which has multiple Start Nodes. GNP is a kind of evolutionary methods inspired from Genetic Programming (GP). While GP has tree architecture, GNP has a network architecture by which a single agent is controlled. In this paper, by increasing the number of Start Nodes in a network of GNP, we extend GNP to control multiple agents. We applied the extended GNP called Multi-Start Node GNP (MS-GNP) to a load transportation problem and a tile world problem.
Kotaro Hirasawa - One of the best experts on this subject based on the ideXlab platform.
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Efficient program generation by evolving graph structures with multi-Start Nodes
Applied Soft Computing, 2011Co-Authors: Shingo Mabu, Kotaro HirasawaAbstract:Automatic program generation is one of the applicable fields of evolutionary computation, and Genetic Programming (GP) is the typical method for this field. On the other hand, Genetic Network Programming (GNP) has been proposed as an extended algorithm of GP in terms of gene structures. GNP is a graph-based evolutionary algorithm and applied to automatic program generation in this paper. GNP has directed graph structures which have some features inherently, for example, re-usability of Nodes and the small number of Nodes. These features contribute to creating complicated programs with compact structures and never cause bloat. In this paper, the extended algorithm of GNP is proposed, which can create plural programs simultaneously in one individual by using multi-Start Nodes. In addition, GNP can evolve the programs in one individual considering the fitness and also its standard deviation in order to evolve the plural programs efficiently. In the simulations, Even-n-Parity problem and Mirror Symmetry problem are used for the performance evaluation, and the results show that the proposed method outperforms the standard GNP with single Start Node.
Shingo Mabu - One of the best experts on this subject based on the ideXlab platform.
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Efficient program generation by evolving graph structures with multi-Start Nodes
Applied Soft Computing, 2011Co-Authors: Shingo Mabu, Kotaro HirasawaAbstract:Automatic program generation is one of the applicable fields of evolutionary computation, and Genetic Programming (GP) is the typical method for this field. On the other hand, Genetic Network Programming (GNP) has been proposed as an extended algorithm of GP in terms of gene structures. GNP is a graph-based evolutionary algorithm and applied to automatic program generation in this paper. GNP has directed graph structures which have some features inherently, for example, re-usability of Nodes and the small number of Nodes. These features contribute to creating complicated programs with compact structures and never cause bloat. In this paper, the extended algorithm of GNP is proposed, which can create plural programs simultaneously in one individual by using multi-Start Nodes. In addition, GNP can evolve the programs in one individual considering the fitness and also its standard deviation in order to evolve the plural programs efficiently. In the simulations, Even-n-Parity problem and Mirror Symmetry problem are used for the performance evaluation, and the results show that the proposed method outperforms the standard GNP with single Start Node.
Afnizanfaizal Abdullah - One of the best experts on this subject based on the ideXlab platform.
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a new weighted pathfinding algorithms to reduce the search time on grid maps
Expert Systems With Applications, 2017Co-Authors: Zeyad Abd Algfoor, Mohd Shahrizal Sunar, Afnizanfaizal AbdullahAbstract:Three new weight techniques for pathfinding algorithm are proposed.Theoretical analysis shows the approximate details strengths/weaknesses of our techniques and previous techniques.Experiment on benchmark pathfinding dataset showed large improvement over existing standard algorithms. Artificial Intelligence (AI) techniques are utilized widely in the field of Expert Systems (ES) - as applied to robotics, video games self-driving vehicles and so on. Pathfinding algorithms are a class of heuristic algorithms based on AI techniques which are used in ES as decision making functions for the purpose of solving problems that would otherwise require human competence or expertise. ES fields that use pathfinding algorithms and operate in real-time face many challenges: for example time constraints, optimality and memory overhead for storing the paths which are found. For these algorithms to work, appropriate problem-specific maps must be constructed. In relation to this, the uniform-cost grid set-up is the most appropriate for ES applications. In this method, each Node in a graph is represented as a tile, and the weight "between" tiles is set at a constant value, usually this is set to 1. In the state-of-the-art heuristic algorithms used with this data structure, multiplying the heuristic function by a weight greater than one is well-known technique. In this paper, we present three new techniques using various weights to accelerate heuristic search of grid maps. The first such technique is based on the iteration of a heuristic search algorithm associated with weight-set w. The second technique is based on the length between the Start Node and goal Node, which is then associated with w. The last technique is based on the travel cost and is associated with a weight-set α. These techniques are applicable to a wide class of heuristic search algorithms. Therefore, we implement them, here, within the A*, the Bidirectional A* (Bi-A*) and Jump Point Search (JPS) algorithms; thus obtaining a family of new algorithms. Furthermore, it is seen that the use of these new algorithms results in significant improvements over current search algorithms. We evaluate them in path-planning benchmarks and show the amended JPS technique's greater stability, across weight values, over the other two techniques. However, it is also shown that this technique yields poor results in terms of cost solution.