scholarly journals Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Pei ◽  
Qiangfu Zhao ◽  
Yong Liu
Keyword(s):  
Control Method ◽  
Fitness Landscape ◽  
Kernel Method ◽  
Subjective Evaluation ◽  
Feature Space ◽  
Search Space ◽  
User Preference ◽  
Human Model ◽  
Accurate Information ◽  
Evolution Control

A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). The fitness landscape of human subjective evaluation in IEC is very difficult and impossible to model, even with a hypothesis of what its definition might be. In this paper, we propose a method to establish a human model in projected high dimensional search space by kernel classification for enhancing IEC search. Because bivalent logic is a simplest perceptual paradigm, the human model is established by considering this paradigm principle. In feature space, we design a linear classifier as a human model to obtain user preference knowledge, which cannot be supported linearly in original discrete search space. The human model is established by this method for predicting potential perceptual knowledge of human. With the human model, we design an evolution control method to enhance IEC search. From experimental evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search significantly.

A New Method for Modeling the Behavior of Finite Population Evolutionary Algorithms

2010 ◽  
Vol 18 (3) ◽  
pp. 451-489 ◽  
Author(s):  
Tatsuya Motoki
Keyword(s):  
Markov Chain ◽  
Evolutionary Algorithms ◽  
Finite Population ◽  
Fitness Landscape ◽  
Markov Chain Model ◽  
Search Space ◽  
Chain Model ◽  
Transition Matrices ◽  
Memory Space ◽  
Exact Model

As practitioners we are interested in the likelihood of the population containing a copy of the optimum. The dynamic systems approach, however, does not help us to calculate that quantity. Markov chain analysis can be used in principle to calculate the quantity. However, since the associated transition matrices are enormous even for modest problems, it follows that in practice these calculations are usually computationally infeasible. Therefore, some improvements on this situation are desirable. In this paper, we present a method for modeling the behavior of finite population evolutionary algorithms (EAs), and show that if the population size is greater than 1 and much less than the cardinality of the search space, the resulting exact model requires considerably less memory space for theoretically running the stochastic search process of the original EA than the Nix and Vose-style Markov chain model. We also present some approximate models that use still less memory space than the exact model. Furthermore, based on our models, we examine the selection pressure by fitness-proportionate selection, and observe that on average over all population trajectories, there is no such strong bias toward selecting the higher fitness individuals as the fitness landscape suggests.

Algebraic Theory of Recombination Spaces

1997 ◽  
Vol 5 (3) ◽  
pp. 241-275 ◽  
Author(s):  
Peter F. Stadler ◽  
Günter P. Wagner
Keyword(s):  
Nearest Neighbor ◽  
Fitness Landscape ◽  
Algebraic Theory ◽  
Algebraic Approach ◽  
Search Space ◽  
Space Structure ◽  
Fitness Landscapes ◽  
Mathematical Representation ◽  
Laplacian Operator ◽  
Fourier Decomposition

A new mathematical representation is proposed for the configuration space structure induced by recombination, which we call “P-structure.” It consists of a mapping of pairs of objects to the power set of all objects in the search space. The mapping assigns to each pair of parental “genotypes” the set of all recombinant genotypes obtainable from the parental ones. It is shown that this construction allows a Fourier decomposition of fitness landscapes into a superposition of “elementary landscapes.” This decomposition is analogous to the Fourier decomposition of fitness landscapes on mutation spaces. The elementary landscapes are obtained as eigenfunctions of a Laplacian operator defined for P-structures. For binary string recombination, the elementary landscapes are exactly the p-spin functions (Walsh functions), that is, the same as the elementary landscapes of the string point mutation spaces (i.e., the hypercube). This supports the notion of a strong homomorphism between string mutation and recombination spaces. However, the effective nearest neighbor correlations on these elementary landscapes differ between mutation and recombination and among different recombination operators. On average, the nearest neighbor correlation is higher for one-point recombination than for uniform recombination. For one-point recombination, the correlations are higher for elementary landscapes with fewer interacting sites as well as for sites that have closer linkage, confirming the qualitative predictions of the Schema Theorem. We conclude that the algebraic approach to fitness landscape analysis can be extended to recombination spaces and provides an effective way to analyze the relative hardness of a landscape for a given recombination operator.

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning

2000 ◽  
Vol 8 (1) ◽  
pp. 61-91 ◽  
Author(s):  
Peter Merz ◽  
Bernd Freisleben
Keyword(s):  
Local Search ◽  
Fitness Landscape ◽  
Memetic Algorithm ◽  
Search Space ◽  
Memetic Algorithms ◽  
Dependency Graph ◽  
Gene Interactions ◽  
Local Minima ◽  
Problem Size ◽  
Better Than

The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a solution in an evolutionary algorithm, the number of local minima for one type of instance decreases and, thus, the search becomes easier. We suggest that other characteristics besides high epistasis might have greater influence on the hardness of a problem. To understand these characteristics, the notion of a dependency graph describing gene interactions is introduced. In particular, the local structure and the regularity of the dependency graph seems to be important for the performance of an algorithm, and in fact, algorithms that exploit these properties perform significantly better than others which do not. It will be shown that a simple hybrid multi-start local search exploiting locality in the structure of the graphs is able to find optimum or near optimum solutions very quickly. However, if the problem size increases or the graphs become unstructured, a memetic algorithm (a genetic algorithm incorporating local search) is shown to be much more effective.

EFFICIENT APPROACH FOR WEB SERVICES SELECTION WITH MULTI-QoS CONSTRAINTS

2008 ◽  
Vol 17 (03) ◽  
pp. 349-371 ◽  
Author(s):  
TAO HUANG ◽  
LEI LI ◽  
JUN WEI
Keyword(s):  
Web Services ◽  
Polynomial Time ◽  
High Performance ◽  
Feasible Solution ◽  
Search Space ◽  
User Preference ◽  
Composite Service ◽  
Efficient Approach ◽  
Complete Problem ◽  
Np Complete

With the increasing number of Web Services with similar or identical functionality, the non-functional properties of a Web Service will become more and more important. Hence, a choice needs to be made to determine which services are to participate in a given composite service. In general, multi-QoS constrained Web Services composition, with or without optimization, is a NP-complete problem on computational complexity that cannot be exactly solved in polynomial time. A lot of heuristics and approximation algorithms with polynomial- and pseudo-polynomial-time complexities have been designed to deal with this problem. However, these approaches suffer from excessive computational complexities that cannot be used for service composition in runtime. In this paper, we propose a efficient approach for multi-QoS constrained Web Services selection. Firstly, a user preference model was proposed to collect the user's preference. And then, a correlation model of candidate services are established in order to reduce the search space. Based on these two model, a heuristic algorithm is then proposed to find a feasible solution for multi-QoS constrained Web Services selection with high performance and high precision. The experimental results show that the proposed approach can achieve the expecting goal.

Research on Method of Improving Sound Property of Vacuum Cleaner by Means of Sound Forge Technology

2011 ◽  
Vol 308-310 ◽  
pp. 197-200
Author(s):  
Li Fang Yang ◽  
Wen Jiao Ding
Keyword(s):  
Frequency Spectrum ◽  
Control Method ◽  
Subjective Evaluation ◽  
Virtual Simulation ◽  
Special Method ◽  
Noise Strength ◽  
Vacuum Cleaner ◽  
Household Appliances ◽  
Spectrum Characteristics ◽  
Good Feeling

Good sound-feeling is more important than noise strength for industrial products, especially for household appliances. In the paper, the control method of good sound-feeling of vacuum cleaner is researched based on the theory of soundscape. For the purpose, a special method of sound forge and evaluation technology (SFET) is utilized. For the method, first the spectrum of vacuum cleaner is analyzed to obtain its spectrum characteristics. Second by means of spectrum forge technology, the frequency spectrum of vacuum cleaner is modified and compiled to make the testing samples. Third based on the sound subjective evaluation, the optimum compiled sound sample of vacuum cleaner is acquired. At last, according to the optimum sound sample, the structure of product is modified to obtain product sound of good-feeling. In the paper, the spectrum of vacuum cleaner noise is analyzed, according to the spectrum, the method of sound forge is discussed. In the end of the paper, the virtual simulation of soundscape of vacuum cleaner is designed by means of some software to give an example of optimizing soudscape.

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.

Power Assist Control Calculated by a Human Model and Joint Angles for Walking Motion Using Pneumatic Actuators

2013 ◽  
Vol 25 (6) ◽  
pp. 915-922 ◽  
Author(s):  
Motonobu Sato ◽  
◽  
Eiichi Yagi ◽  
Kazuo Sano ◽  
Keyword(s):  
Control Method ◽  
The Body ◽  
Human Model ◽  
Joint Angles ◽  
Pneumatic Actuators ◽  
Joint Torques ◽  
Rigid Link ◽  
Walking Motion ◽  
Link Model

This paper describes a method for power assist control that calculates the joint torques necessary to support an assist suit wearer’s walking. We approximate the body using a multijoint rigid link model. Joint support torques are calculated based on this model using the hip, knee and ankle angles of the wearer. Results of experiments show the effectiveness of proposed control method.

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