A Study on Deriving a Method for Chromosome Similarities Suitable for the Search Space

2002 ◽  
Vol 6 (3) ◽  
pp. 135-144 ◽  
Author(s):  
Yoshifumi Banno ◽  
◽  
Tomohiro Yoshikawa ◽  
Hiroharu Kawanaka ◽  
Tsuyoshi Shinogi ◽  
...  
Keyword(s):  
Nonlinear Problems ◽  
Search Space ◽  
Search Algorithms ◽  
Evolutionary Computations ◽  
Evaluation Time ◽  
Fitness Value ◽  
Effective Fitness

Evolutionary Computations (ECs) are powerful search algorithms for nonlinear problems, and have been. widely studied. Generally, calculation of EC to acquire expected solutions takes much time because they need repeated calculation for search solutions. Hanaki et al. proposed the fitness inference to reduce evaluation time by simplifying calculation of the fitness of chromosomes. In fitness inference, how chromosome similarities is defined is very important corresponding to that of solutions. We studied chromosome similarities considering the solution similarities, and propose new chromosome similarities with the weight on each locus determined by the change of information on the locus using Sequential Difference Fitness Value Allocation. We used benchmark functions to study the feasibility of the proposed method and found that effective weights on loci suitable for the search space are automatically generated, and that the proposed method enables effective fitness inference.

Optimization of the Closure-Weld Region of Cylindrical Containers for Long-Term Corrosion Resistance Using the Successive Heuristic Quadratic Approximation Technique*

2003 ◽  
Vol 125 (3) ◽  
pp. 533-539 ◽  
Author(s):  
Zekai Ceylan ◽  
Mohamed B. Trabia
Keyword(s):  
Finite Element ◽  
Random Search ◽  
Nonlinear Problems ◽  
Search Space ◽  
Induction Coil ◽  
Quadratic Approximation ◽  
Approximation Technique ◽  
General Corrosion ◽  
Highly Nonlinear

Welded cylindrical containers are susceptible to stress corrosion cracking (SCC) in the closure-weld area. An induction coil heating technique may be used to relieve the residual stresses in the closure-weld. This technique involves localized heating of the material by the surrounding coils. The material is then cooled to room temperature by quenching. A two-dimensional axisymmetric finite element model is developed to study the effects of induction coil heating and subsequent quenching. The finite element results are validated through an experimental test. The container design is tuned to maximize the compressive stress from the outer surface to a depth that is equal to the long-term general corrosion rate of the container material multiplied by the desired container lifetime. The problem is subject to several geometrical and stress constraints. Two different solution methods are implemented for this purpose. First, an off-the-shelf optimization software is used. The results however were unsatisfactory because of the highly nonlinear nature of the problem. The paper proposes a novel alternative: the Successive Heuristic Quadratic Approximation (SHQA) technique. This algorithm combines successive quadratic approximation with an adaptive random search within varying search space. SHQA promises to be a suitable search method for computationally intensive, highly nonlinear problems.

Phase Transitions for Quantum Search Algorithms

2005 ◽  
Author(s):  
Tad Hogg
Keyword(s):  
Phase Transitions ◽  
Computational Cost ◽  
Search Space ◽  
Search Algorithms ◽  
Quantum Search ◽  
Combinatorial Search ◽  
Problem Structure ◽  
Search Problems ◽  
Quantum Search Algorithms ◽  
The Many

Phase transitions have long been studied empirically in various combinatorial searches and theoretically in simplified models [91, 264, 301, 490]. The analogy with statistical physics [397], explored throughout this volume, shows how the many local choices made during search relate to global properties such as the resulting search cost. These studies have led to a better understanding of typical search behaviors [514] and improved search methods [195, 247, 261, 432, 433]. Among the current research questions in this field are the range of algorithms exhibiting the transition behavior and the algorithm-independent problem properties associated with the difficult instances concentrated near the transition. Towards this end, the present chapter examines quantum computer [123, 126, 158, 486] algorithms for nondeterministic polynomial (NP) combinatorial search problems [191]. As with many conventional methods, they exhibit the easy-hard-easy pattern of computational cost as the degree of constraint in the problems varies. We describe how properties of the search space affect the algorithms and identify an additional structural property, the energy gap, motivated by one quantum algorithm but applicable to a variety of techniques, both quantum and classical. Thus, the study of quantum search algorithms not only extends the range of algorithms exhibiting phase transitions, but also helps identify underlying structural properties. Specifically, the next two sections describe a class of hard search problems and the form of quantum search algorithms proposed to date. The remainder of the chapter presents algorithm behaviors, relevant problem structure, arid an approximate asymptotic analysis of their cost scaling. The final section discusses various open issues in designing and evaluating quantum algorithms, and relating their behavior to problem structure. The k-satisfiability (k -SAT) problem, as discussed earlier in this volume, consists of n Boolean variables and m clauses. A clause is a logical OR of k variables, each of which may be negated. A solution is an assignment, that is, a value for each variable, TRUE or FALSE, satisfying all the clauses. An assignment is said to conflict with any clause it does not satisfy.

Exploring Coverage within Wireless Sensor Networks through Evolutionary Computations

2010 ◽  
pp. 191-200
Author(s):  
Sami Habib
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Search Space ◽  
Service Area ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Evolutionary Computations ◽  
Placement Problem ◽  
Sensor Devices ◽  
Computationally Intensive

The evolutionary search approach has demonstrated its effectiveness in many real world applications, such as the coverage problem in wireless sensor networks. It is to place sensor devices in a service area so that the entire service area is covered. We have modeled the coverage problem as two sub-problems: floorplan and placement. The floorplan problem is to partition the service area into well-defined geometric cells, where the placement problem is to assign the sensor devices into a set of cells. Even though the search space has been transformed from continuous into discrete, the complexity of the coverage problem is computationally intensive. The objective function is to maximize the coverage of the service area while not exceeding a given budget. The merged optimization problem has been coded into the genetic algorithm (GA) and the experimental results reveal the versatility of GA to adapt and find a good solution in a short time.

Parallel Scatter Search Algorithms for Exam Timetabling

2011 ◽  
Vol 2 (3) ◽  
pp. 27-44 ◽  
Author(s):  
Nashat Mansour ◽  
Ghia Sleiman-Haidar
Keyword(s):  
Heuristic Algorithms ◽  
Scatter Search ◽  
Search Space ◽  
Population Based ◽  
Search Algorithms ◽  
Sequential Algorithm ◽  
Solution Quality ◽  
Reasonable Time ◽  
Exam Timetabling ◽  
Communication Techniques

University exam timetabling refers to scheduling exams into predefined days, time periods and rooms, given a set of constraints. Exam timetabling is a computationally intractable optimization problem, which requires heuristic techniques for producing adequate solutions within reasonable execution time. For large numbers of exams and students, sequential algorithms are likely to be time consuming. This paper presents parallel scatter search meta-heuristic algorithms for producing good sub-optimal exam timetables in a reasonable time. Scatter search is a population-based approach that generates solutions over a number of iterations and aims to combine diversification and search intensification. The authors propose parallel scatter search algorithms that are based on distributing the population of candidate solutions over a number of processors in a PC cluster environment. The main components of scatter search are computed in parallel and efficient communication techniques are employed. Empirical results show that the proposed parallel scatter search algorithms yield good speed-up. Also, they show that parallel scatter search algorithms improve solution quality because they explore larger parts of the search space within reasonable time, in contrast with the sequential algorithm.

scholarly journals Constrained Novelty Search: A Study on Game Content Generation

2015 ◽  
Vol 23 (1) ◽  
pp. 101-129 ◽  
Author(s):  
Antonios Liapis ◽  
Georgios N. Yannakakis ◽  
Julian Togelius
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Selection Method ◽  
Search Algorithms ◽  
Genetic Operators ◽  
Search Methods ◽  
Search Spaces ◽  
Novelty Search ◽  
Feasible Space ◽  
Content Generation

Novelty search is a recent algorithm geared toward exploring search spaces without regard to objectives. When the presence of constraints divides a search space into feasible space and infeasible space, interesting implications arise regarding how novelty search explores such spaces. This paper elaborates on the problem of constrained novelty search and proposes two novelty search algorithms which search within both the feasible and the infeasible space. Inspired by the FI-2pop genetic algorithm, both algorithms maintain and evolve two separate populations, one with feasible and one with infeasible individuals, while each population can use its own selection method. The proposed algorithms are applied to the problem of generating diverse but playable game levels, which is representative of the larger problem of procedural game content generation. Results show that the two-population constrained novelty search methods can create, under certain conditions, larger and more diverse sets of feasible game levels than current methods of novelty search, whether constrained or unconstrained. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. Additionally, the proposed enhancement of offspring boosting is shown to enhance performance in all cases of two-population novelty search.

scholarly journals Induction as a Search Procedure

2008 ◽  
pp. 166-216 ◽  
Author(s):  
Stasinos Konstantopoulos ◽  
Rui Camacho ◽  
Nuno A. Fonseca ◽  
Vítor Santos Costa
Keyword(s):  
Logic Programming ◽  
Computer Science ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Search Space ◽  
Search Algorithms ◽  
Stochastic Algorithms ◽  
Search Procedure ◽  
Formal Definition ◽  
Version Spaces

This chapter introduces Inductive Logic Programming (ILP) from the perspective of search algorithms in Computer Science. It first briefly considers the Version Spaces approach to induction, and then focuses on Inductive Logic Programming: from its formal definition and main techniques and strategies, to priors used to restrict the search space and optimized sequential, parallel, and stochastic algorithms. The authors hope that this presentation of the theory and applications of Inductive Logic Programming will help the reader understand the theoretical underpinnings of ILP, and also provide a helpful overview of the State-of-the-Art in the domain.

scholarly journals Optimal Pair DNA Sequence Alignment based on Matching Regions and Multi-Zone Genetic Algorithm

2018 ◽  
Vol 7 (4.19) ◽  
pp. 751
Author(s):  
Sara Q. Abedulridha ◽  
Eman S. Al-Shamery
Keyword(s):  
Genetic Algorithm ◽  
Dna Sequence ◽  
Sequence Alignment ◽  
Dna Sequences ◽  
Search Space ◽  
Pairwise Sequence Alignment ◽  
Optimal Arrangement ◽  
Fitness Score ◽  
Dna Sequence Alignment ◽  
Fitness Value

DNA sequence alignment is an important and challenging task in Bioinformatics, which is used for finding the optimal arrangement between two sequences. In this paper, two methods are proposed in two stages to solve the pairwise sequence alignment problem. The first method is Matching Regions(MR) concerns on splitting the DNA into regions with adaptive interleaving windows to isolate the DNA tape into matched and non-matched regions. Additionally, a Multi-Zone Genetic Algorithm (MZGA) is proposed as an improved method in the second stage. It consists of segmenting a large non-matched region into smaller search space. Then, the MZGA is implemented in parallel to save time. Genetic Algorithm can be applied as an optimization toolto produce multiple solutions. Furthermore, the improvement focuses on the enhancement of Simple GA operators. In the selection, the population is divided into three Zones according to the fitness score. A new crossover approach is proposed depending on cut-points and location of gaps. The proposed method guarantees that the value of fitness tends to improvement or convergence in each successive generation. Thus, the offspring of populations will have better fitness value. The system has been applied to the real-world dataset of DNA with variable lengths which are ranged from 66 bases up to 26037 bases. As a result, the proposed technique satisfied the best alignment score of the DNA sequences. Finally, it is worth mentioning that the proposed system proved to be generalizable.  

scholarly journals Integrating Learning from Examples into the Search for Diagnostic Policies

2005 ◽  
Vol 24 ◽  
pp. 263-303 ◽  
Author(s):  
V. Bayer-Zubek ◽  
T. G. Dietterich
Keyword(s):  
Decision Making ◽  
Greedy Algorithms ◽  
Search Space ◽  
Search Algorithms ◽  
Systematic Search ◽  
Training Data ◽  
Test Results ◽  
New Family ◽  
Diagnostic Decision ◽  
Training Examples

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO* algorithm to solve this MDP. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on five benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on today's desktop computers.

scholarly journals Open-pFind enables precise, comprehensive and rapid peptide identification in shotgun proteomics

2018 ◽  
Author(s):  
Hao Chi ◽  
Chao Liu ◽  
Hao Yang ◽  
Wen-Feng Zeng ◽  
Long Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Search Algorithm ◽  
Large Fraction ◽  
Olfactory Receptors ◽  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Isotopic Labeling ◽  
Search Space ◽  
Global Scale ◽  
Search Algorithms

ABSTRACTShotgun proteomics has grown rapidly in recent decades, but a large fraction of tandem mass spectrometry (MS/MS) data in shotgun proteomics are not successfully identified. We have developed a novel database search algorithm, Open-pFind, to efficiently identify peptides even in an ultra-large search space which takes into account unexpected modifications, amino acid mutations, semi- or non-specific digestion and co-eluting peptides. Tested on two metabolically labeled MS/MS datasets, Open-pFind reported 50.5‒117.0% more peptide-spectrum matches (PSMs) than the seven other advanced algorithms. More importantly, the Open-pFind results were more credible judged by the verification experiments using stable isotopic labeling. Tested on four additional large-scale datasets, 70‒85% of the spectra were confidently identified, and high-quality spectra were nearly completely interpreted by Open-pFind. Further, Open-pFind was over 40 times faster than the other three open search algorithms and 2‒3 times faster than three restricted search algorithms. Re-analysis of an entire human proteome dataset consisting of ∼25 million spectra using Open-pFind identified a total of 14,064 proteins encoded by 12,723 genes by requiring at least two uniquely identified peptides. In this search results, Open-pFind also excelled in an independent test for false positives based on the presence or absence of olfactory receptors. Thus, a practical use of the open search strategy has been realized by Open-pFind for the truly global-scale proteomics experiments of today and in the future.

scholarly journals Fitness Landscape Analysis and Edge Weighting-Based Optimization of Vehicle Routing Problems

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1363
Author(s):  
László Kovács ◽  
Anita Agárdi ◽  
Tamás Bányai
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Fitness Landscape ◽  
Fitness Function ◽  
Search Space ◽  
Landscape Analysis ◽  
Discrete Optimization Problem ◽  
Routing Problem ◽  
Fitness Landscape Analysis ◽  
Fitness Value

Vehicle routing problem (VRP) is a highly investigated discrete optimization problem. The first paper was published in 1959, and later, many vehicle routing problem variants appeared to simulate real logistical systems. Since vehicle routing problem is an NP-difficult task, the problem can be solved by approximation algorithms. Metaheuristics give a “good” result within an “acceptable” time. When developing a new metaheuristic algorithm, researchers usually use only their intuition and test results to verify the efficiency of the algorithm, comparing it to the efficiency of other algorithms. However, it may also be necessary to analyze the search operators of the algorithms for deeper investigation. The fitness landscape is a tool for that purpose, describing the possible states of the search space, the neighborhood operator, and the fitness function. The goal of fitness landscape analysis is to measure the complexity and efficiency of the applicable operators. The paper aims to investigate the fitness landscape of a complex vehicle routing problem. The efficiency of the following operators is investigated: 2-opt, order crossover, partially matched crossover, cycle crossover. The results show that the most efficient one is the 2-opt operator. Based on the results of fitness landscape analysis, we propose a novel traveling salesman problem genetic algorithm optimization variant where the edges are the elementary units having a fitness value. The optimal route is constructed from the edges having good fitness value. The fitness value of an edge depends on the quality of the container routes. Based on the performed comparison tests, the proposed method significantly dominates many other optimization approaches.

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