Genetic Programmings (GPs) is one of the most powerful evolutionary computation (EC) for software evolution. In ECs, it is difficult to maintain efficient building blocks. In particular, the control of building blocks in the population of genetic programming (GP) is relatively difficult because of tree-shaped individuals and also because of bloat, which is the uncontrolled growth of ineffective code segments in GP. For a variety of reasons, reliable techniques to remove bloat are highly desirable. This paper introduces a novel approach of removing bloat, by proposing a novel GP called “Genetic Programming with Multi-Layered Population Structure (MLPS-GP)” that employs multi-layered population and searches solutions using local search and crossover. The MLPS-GP has no mutation-like operator because such kinds of operators are the source of bloats. We showed that diversity can be maintained well only controlling the tree structures by a well-structured multi-layered population. To confirm the effectiveness of the proposed method, the computational experiments were carried out taking several classical Boolean problems as examples.

Fitness approximation methods in Evolutionary Computation (EC) provide us good results in real-world optimization. On the other hand, little is known about the advantages and disadvantages of each surrogate models. Moreover, the performance of models depends on a structure of original function. Therefore, various kinds of surrogate models can leads to better results. We also have proposed a novel surrogate model which can estimate the only rank of two individuals using Support Vector Machine. In addition, we have proposed EC framework with that model called Fitness Landscape Learning Evolutionary Computation (FLLEC) which has shown good performance. In this paper, we compared two type of evolution control in FLLEC with the computational experiments.

The optimal Exploration Exploitation Trade-off (EE Trade-off) is a fundamental goal in the field of Evolutionary Computation. To achieve the goal, we have proposed P-I similarity index and Dictyostelium based Genetic Algorithm (DGA). P-I similarity index provides an exploitation degree to enable applications to explicitly control EE Trade-off. DGA has specific operators which adopt the life cycle of dictyostelium to trade off between exploration and exploitation. In this study we specify the feature of P-I similarity index and introduce DGA with P-I similarity index. The computational experiments were carried out taking several combinatorial optimization problems as examples to suggest that DGA with P-I similarity index has wide applicability to discrete problems.

Many evolutionary techniques such as genetic algorithms (GAs) employ parameters that facilitate user control of search dynamics. However, these parameters require time-consuming tuning processes to avoid problems such as premature convergence. Unlike many GAs, the Parameter-less Population Pyramid (P3) is an optimization model that avoids premature convergence due to the pyramid-like structure of populations, and thus P3 can be applied to a wide range of problems without parameter tuning. P3 approaches to search would be useful for constructing a novel theory for optimal search using GAs. In this study, we propose a novel technique "analysis based on the Distribution of Inferior Individuals in the local neighborhood" (DII analysis). DII analysis can be applied to local search techniques, including P3. The computational complexity of applied problems can be estimated based on a number of local optima according to the results obtained using DII. We also propose combining P3 with DII analysis (P3-DII), which controls the maximum number of fitness evaluations performed by genetic operators. The computational experiments were carried out taking several combinational problems as examples. According to our experimental results, we demonstrated that P3-DII found several optimal solutions that P3 failed to find.

This paper proposes a self-diagnosable multi-agent system. A self-diagnosable algorithm has been proposed for multi-agent systems. This conventional algorithm makes every agent diagnose all other agents, but it has a problem that the more agents are included in a system, the more communications traffic increases. To solve the problem, this paper proposes a framework of multi-agent system that divides and deals with in parallel domains for mutual diagnosis to mitigate the communications between agents. Our approach introduces middle agents that do not interaction with basic client agents and we have developed a dynamic highly structured system that dynamically reconstitutes system configuration. The proposed method has been applied to a multi-agent system that forms a circle autonomously. Numerical experiments show that the proposed method needs much less communications than the conventional one.

Genetic Algorithms (GAs) are a search and optimization technique based on the mechanism of evolution. Recently, another sort of population-based optimization method called Estimation of Distribution Algorithms (EDAs) have been proposed to solve the GA's defects. Although several comparison studies between GAs and EDAs have been made, little is known about differences of statistical features between them. In this paper, we propose new statistical indices which are based on the concepts of crossover and mutation, used in GAs, to analyze the behavior of the population based optimization techniques. We also show simple results of comparison studies between GAs and the Bayesian Optimization Algorithm (BOA), a well-known Estimation of Distribution Algorithms (EDAs).

Adaptation to dynamic environments is an important application of genetic algorithms (GAs). However, there are many difficulties to apply the GA to dynamic environments. Especially, in online environments, the GA's defects become remarkable because individuals should be evaluated in the real world. We proposes a novel approach to such an online adaptation called the environment identifying genetic algorithm (EIGA). Computer simulation is carried out by taking an Nk-landscape problem as an example.

In order to realize seamless integration of paper and electronic documents, it is at least necessary to assure error free conversion from one to the other. In general, the conversion from paper to electronic documents is the task of document image understanding. Although its research has made remarkable progress, it is still a hard task without limiting the type of documents. This paper presents a completely different approach to this task on condition that printed documents have their originals in electronic form. The proposed method employs fine dots to represent data of electronic documents and places the dots on white space (backgrounds) of pages. Since the data is encoded with an error correcting code, it is guaranteed to be correctly recovered from the scanned images of documents. Experimental results show that a page with normal foreground objects (characters and other things) can contain more than 4KB of data, even when errors up to 20% of the data are permitted. 1.

Document image retrieval is a task to retrieve document images relevant to a user's query. Most existing methods based on word-level indexing rely on the representation called "bag of words" which originated in the field of information retrieval. This paper presents a new representation of documents that utilizes additional information about the location of words in pages so as to improve the retrieval performance. We consider that pages are relevant to a query if they contain its terms densely. This notion is embodied as density distributions of terms calculated in the proposed method. Its performance is improved with the help of "pseudo relevance feedback", i.e., a method of expanding a query by analyzing pages. Experimental results on English document images show that the proposed method is superior to conventional methods of electronic document retrieval at recall levels 0.0-0.6.

Retrieval of electronic documents is a fundamental component for intelligent access to the contents of documents. For the retrieval of long documents, a method called passage-based document retrieval has proven to be effective. In this paper we experimentally show that the passage-based retrieval is also advantageous for dealing with short queries on condition that documents are long. We employ a passage-based method based on density distributions of query terms in documents, and compare it with three conventional methods: the vector space model, pseudo-feedback and latent semantic indexing.

This paper presents a method of embedding and recovery of a large amount of data on printed documents. The proposed method is characterized by the following points: (1) As the medium for recording the data, backgrounds (white space) of pages are utilized. Since white space is dominant in ordinary pages, this enables us to record a large amount of data. (2) As the representation of data, an arbitrary figure (such as a logo mark of a company) is stippled to obtain a set of points, each of which is superimposed on a page image. This allows us to prevent the appearance of a page from deteriorating, since stippled figures play a role of background patterns on printed documents. The experimental results show that the method is capable of both recording 7.8KB of data and reading the embedded data with the accuracy of 99%.

This paper presents a method of automated generation of hypertext links for electronic documents. The goal is to generate links from an arbitrary part of a document (a source of a link) to its relevant parts of target documents (destinations). To achieve this goal, we assume that words are often shared by parts of documents if these parts are relevant with each other. In order to extract parts densely including words of a source (keywords), we employ density distributions of keywords. This enables us to determine destinations simply by extracting parts whose density exceeds a threshold. Experiments on generating links from figures/tables to parts of documents, as well as from texts to parts of different documents show that our method with the optimal parameters yields recall of 60% and precision of 50%.

The background of power system restoration is analyzed and how this process might benefit from the use of knowledge-based systems is discussed. After describing the state of the art of knowledge-based systems for power system restoration, efforts are made to review knowledge-based techniques to handle system restoration problems. The experiences of system development are reported and evaluation of the systems is addressed. Some needed technological innovations are listed that are required before knowledge-based systems can extensively be applied to power system restoration.>

Arrow's general impossibility theorem shows that it is impossible in a general sense (under Arrow's five axioms) to aggregate individuals' value judgments into a social one [1]. In group decisionmaking, therefore, it seems rather important to analyze the group dynamics and/or to refine the information available for decisionmakers to help their mutual understanding. With this in mind, a method is suggested supporting group decision, which is called the extended contributive rule (ECR) method on the basis of the multiattribute utility theory and the interpretive structural modeling (ISM) method. In addition, the stated method will be applied to the assessment of the residential environment in Kyoto City in Japan.