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This paper points out that meta-heuristics should have not only robustness and adaptability to problems with different structure but also adjustability of parameters included in their algorithms. Particle swarm optimization (PSO), whose concept began as a simulation of a simplified social milieu, is known as one of the most powerful optimization methods for solving nonconvex continuous optimization problems. Then, in order to improve adjustability, a new parameter is introduced into PSO on the basis of the proximate optimality principle (POP). In this paper, we propose adaptive PSO and the effectiveness and the feasibility of the proposed approach are demonstrated on simulations using some typical nonconvex optimization problems. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 151(3): 41–49, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20077 [ABSTRACT FROM AUTHOR]

In [4] the authors studied the residue field of a model M of IΔ0 + Ω1 for the principal ideal generated by a prime p. One of the main results is that M/ has a unique extension of each finite degree. In this paper we are interested in understanding the structure of any quotient field of M, i.e. we will study the quotient M/I for I a maximal ideal of M. We prove that any quotient field of M satisfies the property of having a unique extension of each finite degree. We will use some of Cherlin's ideas from [3], where he studies the ideal theory of non standard algebraic integers. [ABSTRACT FROM AUTHOR]

The paper presents an analytical description of a state-dependent assignment policy, where different assignments of attributes to objects are employed under different states. Various considerations, other than optimizing an objective function, present themselves for such cases—for example, the existence of groups of assignees with certain levels of intragroup assignment uniformity, the stability of the policy over states, the presence of a revealed preference ordering of the assignments, and others. An approach towards a quantitative treatment of such features of a state-dependent assignment policy is discussed, and in particular a uniformity measure is defined and its properties are investigated. Potential uses of the model are suggested for urban emergency services, questionnaire analysis, task allocation, medical diagnosis, and dynamic computer program behavior. Three implementations for questionnaire analysis are reported. [ABSTRACT FROM AUTHOR]

In [4] the authors studied the residue field of a model M of IΔ0 + Ω1 for the principal ideal generated by a prime p. One of the main results is that M/ has a unique extension of each finite degree. In this paper we are interested in understanding the structure of any quotient field of M, i.e. we will study the quotient M/I for I a maximal ideal of M. We prove that any quotient field of M satisfies the property of having a unique extension of each finite degree. We will use some of Cherlin's ideas from [3], where he studies the ideal theory of non standard algebraic integers. [ABSTRACT FROM AUTHOR]

Feature selection is one of the most important processes in the design of pattern classifiers. This paper presents an optimum feature selection method which is applicable to arbitrary (nonlinear) decision functions. It is assumed that a finite number of trains lag samples (training set) is given for each pattern class, and the decision function is designed based on the training sets. The training sets are edited by renoving the samples which are classified incorrectly by the decision function. That the feature selection problem is transformed to a modified zero-one integer program. In this method, under a chosen permissible error, a minimum feature subset can be found which is combinationally optimum. Numerical, examples of feature selection I or a linear and a quadratic decision function are presented. [ABSTRACT FROM AUTHOR]