Scheduling Algorithms for Information and Communication Systems [Working Title]
In this chapter, we propose a novel algorithm that uses Genetic algorithm with group theory for i... more In this chapter, we propose a novel algorithm that uses Genetic algorithm with group theory for initial population generation and also propose a novel crossover for solving Traveling Salesman Problem. In the group tour construction method, each individual/initial tour has distinct start city provided that population size is equal to total number of cities. In the initial population, each individual/tour has a distinct starting city. The distinct starting cites of each tour provide genetic material for exploration for the whole search space. Therefore, a heterogeneous starting city of a tour in initial population is generated to have rich diversity. Proposed crossover based on greedy method of sub-tour connection drives the efficient local search, followed by 2-opt mutation for improvement of tour for enhanced/optimal solution. The result of the proposed algorithm is compared with other standard algorithms followed by conclusion.
In this paper, we proposed a new crossover operator and a population initialization method for so... more In this paper, we proposed a new crossover operator and a population initialization method for solving multiple traveling salesmen (MTSP) problem in genetic algorithm (GA) framework. The group theory based technique of intital population generation ensures the uniqueness of members in population, hence no redundancy in the search space and also remove the random initialization effect. The new crossover is based on the intuitive idea that the city in sub optimal / optimal tours occurs at same position. In this crossover the Hamming distance is preserved and there is very less chance to generate child same as members in the population, so diversity of the population is not much affected. For efficient representation of search space, we exploited the multi-chromosome representation technique to encode the search space of MTSP. We evaluate and compare the proposed technique with the methods using crossover TCX, ORX +A, CYX +A and PMX +A reported in [35] for two standard objective functions of the MTSP, namely, minimising total travel cost of m tours of the m salesman and minimising the longest tour cast travel by any one salesman. Experimental results show that the GA with proposed population initialization and crossover gives better result compared to all four methods for second objective, however, in very few cases slightly degraded result for first objective.
Since decades developing programs for board games has been part of AI research and this field has... more Since decades developing programs for board games has been part of AI research and this field has attracted computer developers and researchers world-wide. Board games have a novel feature of simple, precise, easily formalized rules which makes ...
The soft computing approach for gaming is different from the traditional one that exploits knowle... more The soft computing approach for gaming is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages. It is aims to evolve efficiently some simple heuristics that can be created easily from the basic knowledge of ...
Soft Computing branch of intelligence research is primarily focused on the path of achieving high... more Soft Computing branch of intelligence research is primarily focused on the path of achieving high performance by mimicking the human approach. The central idea is to capture and encode human knowledge in artificial learning form. Applying AI technology to develop efficient game-playing programs is through realization of search-intensive approach in very large and complex search intensive areas. Many researchers have developed very powerful search techniques over the past two decades and successfully applied these search algorithms to problems domains of optimization, machine learning and soft computing paradigms. This paper extends this approach, by developing a program which is almost completely reliant on search optimization through evolutionary computation. Very efficient evolutionary algorithms and advancement of "intelligent" search along with improved hardware resources like faster processors, larger memories, and larger disks makes it possible to push the limits to ...
One of the areas of Artificial intelligence is Board Game Playing. Game-playing programs are ofte... more One of the areas of Artificial intelligence is Board Game Playing. Game-playing programs are often described as being a combination of search and knowledge. The board games are very popular. Board Games provide dynamic environments that make them ...
Scheduling Algorithms for Information and Communication Systems [Working Title]
In this chapter, we propose a novel algorithm that uses Genetic algorithm with group theory for i... more In this chapter, we propose a novel algorithm that uses Genetic algorithm with group theory for initial population generation and also propose a novel crossover for solving Traveling Salesman Problem. In the group tour construction method, each individual/initial tour has distinct start city provided that population size is equal to total number of cities. In the initial population, each individual/tour has a distinct starting city. The distinct starting cites of each tour provide genetic material for exploration for the whole search space. Therefore, a heterogeneous starting city of a tour in initial population is generated to have rich diversity. Proposed crossover based on greedy method of sub-tour connection drives the efficient local search, followed by 2-opt mutation for improvement of tour for enhanced/optimal solution. The result of the proposed algorithm is compared with other standard algorithms followed by conclusion.
In this paper, we proposed a new crossover operator and a population initialization method for so... more In this paper, we proposed a new crossover operator and a population initialization method for solving multiple traveling salesmen (MTSP) problem in genetic algorithm (GA) framework. The group theory based technique of intital population generation ensures the uniqueness of members in population, hence no redundancy in the search space and also remove the random initialization effect. The new crossover is based on the intuitive idea that the city in sub optimal / optimal tours occurs at same position. In this crossover the Hamming distance is preserved and there is very less chance to generate child same as members in the population, so diversity of the population is not much affected. For efficient representation of search space, we exploited the multi-chromosome representation technique to encode the search space of MTSP. We evaluate and compare the proposed technique with the methods using crossover TCX, ORX +A, CYX +A and PMX +A reported in [35] for two standard objective functions of the MTSP, namely, minimising total travel cost of m tours of the m salesman and minimising the longest tour cast travel by any one salesman. Experimental results show that the GA with proposed population initialization and crossover gives better result compared to all four methods for second objective, however, in very few cases slightly degraded result for first objective.
Since decades developing programs for board games has been part of AI research and this field has... more Since decades developing programs for board games has been part of AI research and this field has attracted computer developers and researchers world-wide. Board games have a novel feature of simple, precise, easily formalized rules which makes ...
The soft computing approach for gaming is different from the traditional one that exploits knowle... more The soft computing approach for gaming is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages. It is aims to evolve efficiently some simple heuristics that can be created easily from the basic knowledge of ...
Soft Computing branch of intelligence research is primarily focused on the path of achieving high... more Soft Computing branch of intelligence research is primarily focused on the path of achieving high performance by mimicking the human approach. The central idea is to capture and encode human knowledge in artificial learning form. Applying AI technology to develop efficient game-playing programs is through realization of search-intensive approach in very large and complex search intensive areas. Many researchers have developed very powerful search techniques over the past two decades and successfully applied these search algorithms to problems domains of optimization, machine learning and soft computing paradigms. This paper extends this approach, by developing a program which is almost completely reliant on search optimization through evolutionary computation. Very efficient evolutionary algorithms and advancement of "intelligent" search along with improved hardware resources like faster processors, larger memories, and larger disks makes it possible to push the limits to ...
One of the areas of Artificial intelligence is Board Game Playing. Game-playing programs are ofte... more One of the areas of Artificial intelligence is Board Game Playing. Game-playing programs are often described as being a combination of search and knowledge. The board games are very popular. Board Games provide dynamic environments that make them ...
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Papers by Dharm Singh