Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Performance in searching solutions by Multi-Objective Genetic Algorithm (MOGA) depends on genetic operators and/or their parameters. For comparison of the performance with some genetic operators and/or parameters, it has been usually employed the transitions of fitness values through actual applications or the number/performance of acquired Pareto solutions in multi-optimization problems.  proposes a visualizing method of search process for MOGA, which can visualize relative distances among chromosomes in search process and give information of not only the performance but also the effects of the genetic operations such as the diversity of chromosomes. This method uses Self-Organizing Map (SOM) for the visualization.  applies Non Dominated Sorting Genetic Algorithm-II (NSGA-II) to ZDT2 and FON test functions and shows obtained nondominated solutions and visualization results.  also shows that the visualized data enables us to interpret the differences in search processes and to get new information to determine efficient genetic operators and their parameters.
['Daisuke Yamashiro', 'Tomohiro Yoshikawa', 'Takeshi Furuhashi']
I am writing a research paper on a idea Visualization of Search Process and Improvement of Search Performance in Multi-Objective Genetic Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Visualization of Search Process and Improvement of Search Performance in Multi-Objective Genetic Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Performance in searching solutions by Multi-Objective Genetic Algorithm (MOGA) depends on genetic operators and/or their parameters. For comparison of the performance with some genetic operators and/or parameters, it has been usually employed the transitions of fitness values through actual applications or the number/performance of acquired Pareto solutions in multi-optimization problems.  proposes a visualizing method of search process for MOGA, which can visualize relative distances among chromosomes in search process and give information of not only the performance but also the effects of the genetic operations such as the diversity of chromosomes. This method uses Self-Organizing Map (SOM) for the visualization.  applies Non Dominated Sorting Genetic Algorithm-II (NSGA-II) to ZDT2 and FON test functions and shows obtained nondominated solutions and visualization results.  also shows that the visualized data enables us to interpret the differences in search processes and to get new information to determine efficient genetic operators and their parameters."}]