Abstract: A system for executing a learning algorithm for solving an optimization problem of an evolutionary algorithm, etc. In this system, a learning algorithm executing device and a terminal device are interconnected by a network. In the terminal device, a change of an execution process of the learning algorithm may be set during the execution of the learning algorithm. In the learning algorithm executing device, the execution of the learning algorithm is continued according to the change of the execution process, which is set in the terminal device. As the change of the execution process of the learning algorithm, an execution condition of the learning algorithm is changed during its execution, or a visual or numerical change is made to an object representing the progress state of the execution of the algorithm on a display screen, in the terminal device.

Abstract: A problem solving operation apparatus obtains an optimum solution for a given problem through a combined use of a plurality of optimum solution search techniques of a genetic algorithm, and other search techniques like a hill climbing method. A combination of the optimum solution search techniques applied to each of a group of solution candidates varies according to each state. When a search efficiency in a single state is lowered, the problem solving operation apparatus immediately causes a state transition to the next state, thus enabling a search of an optimum solution through the use of another combination of more efficient optimum solution search techniques. Because a state transition constantly maintains a high search efficiency, this problem solving operation apparatus can reach an optimum solution in a short amount of time.