Search results for "A* algorithm"

Non-syndromic Mitral Valve Dysplasia Mutation Changes the Force Resilience and Interaction of Human Filamin A

2018

International audience; Filamin A (FLNa), expressed in endocardial endothelia during fetal valve morphogenesis, is key in cardiac development. Missense mutations in FLNa cause non-syndromic mitral valve dysplasia (FLNA-MVD). Here, we aimed to reveal the currently unknown underlying molecular mechanism behind FLNA-MVD caused by the FLNa P637Q mutation. The solved crystal structure of the FLNa3-5 P637Q revealed that this mutation causes only minor structural changes close to mutation site. These changes were observed to significantly affect FLNa's ability to transmit cellular force and to interact with its binding partner. The performed steered molecular dynamics simulations showed that signi…

Complexity of probabilistic versus deterministic automata

2005

Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis

2020

Parkinson’s disease is found as a progressive neurodegenerative condition which affects motor circuit by the loss of up to 70% of dopaminergic neurons. Thus, diagnosing the early stages of incidence is of great importance. In this article, a novel chaos-based stochastic model is proposed by combining the characteristics of chaotic firefly algorithm with Kernel-based Naïve Bayes (KNB) algorithm for diagnosis of Parkinson’s disease at an early stage. The efficiency of the model is tested on a voice measurement dataset that is collected from “UC Irvine Machine Learning Repository.” The dynamics of chaos optimization algorithm will enhance the firefly algorithm by introducing six types of chao…

Mind the depth: The vertical dimension of a small-scale coastal fishery shapes selection on species, size, and sex in wrasses

2020

Small‐scale fisheries (SSFs) tend to target shallow waters, but the depth distributions of coastal fish can vary depending on species, size, and sex. This creates a scope for a form of fishing selectivity that has received limited attention but can have considerable implications for monitoring and management of these fisheries. We conducted a case study on the Norwegian wrasse fishery, a developing SSF in which multiple species are caught in shallow waters (mean depth = 4.5 m) to be used as cleaner fish in aquaculture. Several of these wrasses have life histories and behaviors that are sensitive to selective fishing mortality, such as sexual size dimorphism, paternal care, and sex change. A…

Hybrid Genetic Algorithms in Data Mining Applications

2009

Genetic algorithms (GAs) are a class of problem solving techniques which have been successfully applied to a wide variety of hard problems (Goldberg, 1989). In spite of conventional GAs are interesting approaches to several problems, in which they are able to obtain very good solutions, there exist cases in which the application of a conventional GA has shown poor results. Poor performance of GAs completely depends on the problem. In general, problems severely constrained or problems with difficult objective functions are hard to be optimized using GAs. Regarding the difficulty of a problem for a GA there is a well established theory. Traditionally, this has been studied for binary encoded …

A genetic algorithm for image segmentation

2002

The paper describes a new algorithm for image segmentation. It is based on a genetic approach that allow us to consider the segmentation problem as a global optimization problem (GOP). For this purpose, a fitness function, based on the similarity between images, has been defined. The similarity is a function of both the intensity and the spatial position of pixels. Preliminary results, obtained using real images, show a good performance of the segmentation algorithm.

Research of a Cellular Automaton Simulating Logic Gates by Evolutionary Algorithms

2003

This paper presents a method of using genetic programming to seek new cellular automata that perform computational tasks. Two genetic algorithms are used : the first one discovers a rule supporting gliders and the second one modifies this rule in such a way that some components appear allowing it to simulate logic gates. The results show that the genetic programming is a promising tool for the search of cellular automata with specific behaviors, and thus can prove to be decisive for discovering new automata supporting universal computation.

Behavior adaptation and selection.

2010

6 pages; The evolutionary approach to behavior is concerned with the evolutionary origin and adaptive function of behavioral traits. Like any other part of the phenotype, behavior can be shaped by natural selection to produce adaptations. However, behavior often shows large phenotypic variation and flexibility, and can be both – subject to selection and a major agent of selection. Therefore, the study of adaptation and evolution of behavior is a particularly complex one, involving a wide range of methodologies and techniques, including mathematical modeling, comparative methods, phenotypic engineering, quantitative genetics, genetic dissection, and artificial selection.

Real-Time Routing Selection in Flexible Manufacturing Systems

1993

Routing flexibility is one of the main peculiarities of Flexible Manufacturing Systems. This paper proposes three methods for real-time routing selection. The first one makes decisions comparing the current workload of machines in each alternative path. The second method considers the current workloads at the bottleneck machines in each allowed route. The third approach makes real-time decisions minimizing a merit index that represents a measure of the still required resource amount. The index is computed by short discrete-event simulation runs. Some case studies evaluate and compare the proposed approaches.

An Interactive Simple Indicator-Based Evolutionary Algorithm (I-SIBEA) for Multiobjective Optimization Problems

2015

This paper presents a new preference based interactive evolutionary algorithm (I-SIBEA) for solving multiobjective optimization problems using weighted hypervolume. Here the decision maker iteratively provides her/his preference information in the form of identifying preferred and/or non-preferred solutions from a set of nondominated solutions. This preference information provided by the decision maker is used to assign weights of the weighted hypervolume calculation to solutions in subsequent generations. In any generation, the weighted hypervolume is calculated and solutions are selected to the next generation based on their contribution to the weighted hypervolume. The algorithm is compa…