Kozo Okazaki

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Solving the Graph Planarization Problem Using an Improved Genetic Algorithm

An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improved genetic algorithm performs remarkably well and outperforms its competitors.

A hill-jump algorithm of Hopfield neural network for shortest path problem in communication network

In this paper, we present a hill-jump algorithm of the Hopfield neural network for the shortest path problem in communication networks, where the goal is to find the shortest path from a starting node to an ending node. The method is intended to provide a near-optimum parallel algorithm for solving the shortest path problem. To do this, first the method uses the Hopfield neural network to get a path. Because the neural network always falls into a local minimum, the found path is usually not a shortest path. To search the shortest path, the method then helps the neural network jump from local minima of energy function by using another neural network built from a part of energy function of the problem. The method is tested through simulating some randomly generated communication networks, with the simulation results showing that the solution found by the proposed method is superior to that of the best existing neural network based algorithm.

Finding a Near-Maximum Independent Set of a Circle Graph by Using Genetic Algorithm with Conditional Genetic Operators

The maximum independent set problem is of central importance combinatorial optimization problem. It has many practical applications in science and engineering. In this paper, we propose a genetic algorithm based approach to solve the problem. In the proposed approach, the genetic operators are performed basing on condition instead of probability. The proposed algorithm is tested on a large number of instances and the simulation results show that the proposed method is superior to its competitors.

A Genetic Algorithm with Conditional Crossover and Mutation Operators and Its Application to Combinatorial Optimization Problems

In this paper, we present a modified genetic algorithm for solving combinatorial optimization problems. The modified genetic algorithm in which crossover and mutation are performed conditionally instead of probabilistically has higher global and local search ability and is more easily applied to a problem than the conventional genetic algorithms. Three optimization problems are used to test the performances of the modified genetic algorithm. Experimental studies show that the modified genetic algorithm produces better results over the conventional one and other methods.

A Hill-Shift Learning Algorithm of Hopfield Network for Bipartite Subgraph Problem

In this paper, we present a hill-shift learning method of the Hopfield neural network for bipartite subgraph problem. The method uses the Hopfield neural network to get a near-maximum bipartite subgraph, and shifts the local minimum of energy function by adjusts the balance between two terms in the energy function to help the network escape from the state of the near-maximum bipartite subgraph to the state of the maximum bipartite subgraph or better one. A large number of instances are simulated to verify the proposed method with the simulation results showing that the solution quality is superior to that of best existing parallel algorithm.

Electrical Impedance Tomography for Washing out Undergound Pollution

We are going to develop a method of washing out underground pollution by using water permeable pipes developed by us and pouring/draining water. This method is effective even there exist already buildings on the ground. To visualize underground pollution directly or especially underground water which can work as the solvent of pollutant to be washed out, we have developed electrical impedance tomography (EIT) for underground. By quantifying underground water, we can quantify/control the purifying process. Some of the experimental results show its feasibility.