Constraint satisfaction on dynamic environments by the means of coevolutionary genetic algorithms

We discuss adaptability of evolutionary computations in dynamic environments. We introduce two classes of dynamic environments which are utilizing the notion of constraint satisfaction problems: changeover and gradation. The changeover environment is a problem class which consists of a sequence of the constraint networks with the same nature. On the other hand, the gradation environment is a problem class which consists of a sequence of the constraint networks such that the sequence is associated with two constraint networks, i. e., initial and target, and all constraint networks in the sequence metamorphosis from the initial constraint network to the target constraint network. We compare coevolutionary genetic algorithms with SGA in computational simulations. Experimental results on the above dynamic environments confirm us the effectiveness of our approach, i.e., coevolutionary genetic algorithm</p

Range imaging system with multiplexed structured light by direct space encoding

Since practical multiplexed structured light systems currently available use plural light patterns or different illumination conditions to ensure a high reliability, their fast performance is impaired. This paper describes a fast, highly reliable range imaging system with a multiplexed structured light system that uses a direct space encoding approach while using only a single light pattern. Unlike a conventional encoding approach, the proposed approach is unique in that it encodes object space through the use of a special optical system which consists of field stops, plural lenses, and shield masks, rather than a light pattern. The theoretical considerations and experimental results demonstrate that the proposed approach is effective for a highly reliable, fast, accurate range imaging system </p

Film Continuity Problem on Journal Bearing Design

Pressure distribution has been measured and analyzed to clarify the fundamental characteristics of "continuous oil-film" formed in a transparent journal bearing, into which oil in general use is supplied. Measured pressure mostly shows quasi-Sommerfeld distribution, which is characterized by downstream shift of pressure profile and underdevelopment of pressure trough compared with Sommerfeld distribution for perfect oil-film. Sommerfeld distribution is approximately observed only under limited conditions : low eccentricity and low speed. Quasi-Sommerfeld state is rather common in "continuous oil-film", unruptured film formed by using practical lubricants, than Sommerfeld state. Continuous oil-film is accompanied by fine bubbles and is controlled by the growing up or down of the bubbles

A new fast rangefinding method based on a non-mechanical scanning mechanism and a high-speed image sensor

In this paper, we present a new fast rangefinding method based on a non-mechanical scanning mechanism and a high-speed image sensor. Although the light stripe rangefinding method often is utilized to measure three dimensional shape of an object, it is difficult to acquire dense range data at high-speed with conventional light stripe rangefinders. We proposed a fast rangefinding method based on two new ideas unlike conventional methods: (1) to move a parabolic light pattern onto the object by means of a non-mechanical mechanism; (2) to detect a true peak value using a high-speed image sensor. We have designed and built a prototype rangefinder. The rangefinder was able to acquire three-dimensional position at 500 ns which is faster than conventional rangefinders. As a result, the proposed method is effective for high-speed three-dimensional measurement </p

A new position sensor for high-speed measurement of multiple points

A high-speed measuring system of multiple points is becoming an important issue in many industrial applications. Therefore, the development of a high-speed position sensor is an important issue. However, conventional sensors such as CCD(charge-coupled device) and PSD(position-sensitive detector) are insufficient to apply to the high-speed measurement of multiple points. We propose a new position sensor for high-speed measurement of multiple points. The proposed sensor features a single scanning detecting method of multiple points by parallel processing technique and design of the sensor by analog circuitry, which makes high-speed measurement of multiple points possible. The designed sensor system realizes both high-speed performance and high accuracy </p

A new fitness function for discovering a lot of satisfiable solutions in constraint satisfaction problems

In this paper, we discuss how many satisfiable solutions a genetic algorithm can find in a problem instance of a constraint satisfaction problems in a single execution. Hence, we propose a framework for a new fitness function which can be applied to traditional fitness functions. However, the mechanism of the proposed fitness function is quite simple, and several experimental results on a variety of instances of general constraint satisfaction problems demonstrate the effectiveness of the proposed fitness function</p

Perception-action rule acquisition by coevolutionary fuzzy classifier system

Recently, many researchers have studied the techniques in applying a fuzzy classifier system (FCS) to control mobile robots, since the FCS can easily treat continuous inputs, such as sensors and images by using a fuzzy number. By using the FCS, however, only reflective rules are acquired. Thus, in the proposed approach, an additional genetic algorithm is incorporated in order to search for strategic knowledge, i.e., the sequence of effective activated rules in the FCS. Therefore, the proposed method consists of two modules: an ordinal FCS and the genetic algorithm. Computational experiments based on WEBOTS, one of the Khepera robot simulators, confirm the effectiveness of the proposed method</p

Adaptive state construction for reinforcement learning and its application to robot navigation problems

This paper applies our state construction method by ART neural network to robot navigation problems. Agents in this paper consist of ART neural network and contradiction resolution mechanism. The ART neural network serves as a mean of state recognition which maps stimulus inputs to a certain state and state construction which creates a new state when a current stimulus input cannot be categorized into any known states. On the other hand, the contradiction resolution mechanism (CRM) uses agents' state transition table to detect inconsistency among constructed states. In the proposed method, two kinds of inconsistency for the CRM are introduced: &#34;Different results caused by the same states and the same actions&#34; and &#34;Contradiction due to ambiguous states.&#34; The simulation results on the robot navigation problems confirm the effectiveness of the proposed method</p

Coevolutionary genetic algorithm for constraint satisfaction with a genetic repair operator for effective schemata formation

We discuss a coevolutionary genetic algorithm for constraint satisfaction. Our basic idea is to explore effective genetic information in the population, i.e., schemata, and to exploit the genetic information in order to guide the population to better solutions. Our coevolutionary genetic algorithm (CGA) consists of two GA populations; the first GA, called “H-GA”, searches for the solutions in a given environment (problem), and the second GA, called “P-GA”, searches for effective genetic information involved in the H-GA, namely, good schemata. Thus, each individual in P-GA consists of alleles in H-GA or “don't care” symbol representing a schema in the H-GA. These GA populations separately evolve in each genetic space at different abstraction levels and affect with each other by two genetic operators: “superposition” and “transcription”. We then applied our CGA to constraint satisfaction problems (CSPs) incorporating a new stochastic “repair” operator for P-GA to raise the consistency of schemata with the (local) constraint conditions in CSPs. We carried out two experiments: First, we examined the performance of CGA on various “general” CSPs that are generated randomly for a wide variety of “density” and “tightness” of constraint conditions in the CSPs that are the basic measures of characterizing CSPs. Next, we examined “structured” CSPs involving latent “cluster” structures among the variables in the CSPs. For these experiments, computer simulations confirmed us the effectiveness of our CGA</p

An incremental state-segmentation method for reinforcement learning using ART neural network

In this paper, we propose a new incremental state segmentation method by utilizing information of the agents' state transition table which consists of a tuple of (state; action, state) in order to reduce the effort of designers and which is generated using the ART neural network. In the proposed method, if an inconsistent situation in the state transition table is observed, agents refine their map from perceptual inputs to states such that inconsistency is resolved. We introduce two kinds of inconsistency, i.e., different results caused by the same states and the same actions, and contradiction due to ambiguous states. Several computational simulations on cart-pole problems confirm the effectiveness of the proposed method</p