scholarly journals A Simple Fitness Function for Minimum Attribute Reduction

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Yuebin Su ◽  
Jin Guo ◽  
Zejun Li
Keyword(s):  
Heuristic Search ◽  
Optimization Problem ◽  
Fitness Function ◽  
Attribute Reduction ◽  
Optimal Solution ◽  
Boolean Space ◽  
Fitness Functions ◽  
Minimal Subset ◽  
Classification Quality ◽  
Better Than

The goal of minimal attribute reduction is to find the minimal subsetRof the condition attribute setCsuch thatRhas the same classification quality asC. This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approaches were used. In this case, the fitness function is one of the keys of this problem. It required that the fitness function must satisfy the equivalence between the optimal solution and the minimal attribute reduction. Unfortunately, the existing fitness functions either do not meet the equivalence, or are too complicated. In this paper, a simple and better fitness function based on positive domain was given. Theoretical proof shows that the optimal solution is equivalent to minimal attribute reduction. Experimental results show that the proposed fitness function is better than the existing fitness function for each algorithm in test.

scholarly journals Faired MISO B-Spline Fuzzy Systems and Its Applications

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Tan Yanhua ◽  
Li Hongxing
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Optimization Problem ◽  
Inverted Pendulum ◽  
Optimal Solution ◽  
Solution Vector ◽  
B Spline ◽  
Variable Universe ◽  
Inexact Data ◽  
Better Than

We construct two classes of faired MISO B-spline fuzzy systems using the fairing method in computer-aided geometric design (CAGD) for reducing adverse effects of the inexact data. Towards this goal, we generalize the faring method to high-dimension cases so that the faring method only for SISO and DISO B-spline fuzzy systems is extended to fair the MISO ones. Then the problem to construct a faired MISO B-spline fuzzy systems is transformed into solving an optimization problem with a strictly convex quadratic objective function and the unique optimal solution vector is taken as linear combination coefficients of the basis functions for a certain B-spline fuzzy system to obtain a faired MISO B-spline fuzzy system. Furthermore, we design variable universe adaptive fuzzy controllers by B-spline fuzzy systems and faired B-spline fuzzy systems to stabilize the double inverted pendulum. The simulation results show that the controllers by faired B-spline fuzzy systems perform better than those by B-spline fuzzy systems, especially when the data for fuzzy systems are inexact.

Intelligent Optimization of Urban Real-Time Traffic Route Based on DNA Non-Dominated Sorting Genetic Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1746-1749
Author(s):  
Min Zhu ◽  
Bo Su ◽  
Gang Min Ning
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Complexity Analysis ◽  
Fitness Function ◽  
Optimal Solution ◽  
Urban Traffic ◽  
Route Optimization ◽  
Optimization Strategy ◽  
Traffic Condition ◽  
Real Time Traffic

The urban traffic condition is changed timely, so the traditional serial algorithm cannot satisfy the requirement of traffic scale and condition changes. Therefore, this paper proposes a DNA non-dominated sorting genetic algorithm for route optimization problem of multi-objects. First, through Pareto frontiers solution set optimization and algorithm complexity analysis, we determine the multi-objects problem to be optimized. Then we convert the problem into optimization problem of single-object fitness function, namely the elite populations optimization strategy, through which we can obtain the optimal solution of timely traffic condition.

scholarly journals Cryptanalysis of the Columnar Transposition Using Meta-Heuristics

2019 ◽  
Vol 73 (1) ◽  
pp. 39-60
Author(s):  
Eugen Antal ◽  
Peter Javorka ◽  
Tomáš Hliboký
Keyword(s):  
Global Optimization ◽  
Detailed Analysis ◽  
Optimization Problem ◽  
Fitness Landscape ◽  
Fitness Function ◽  
Heuristic Methods ◽  
Function Form ◽  
Fitness Landscape Analysis ◽  
Fitness Functions ◽  
Open Questions

Abstract The most commonly used methods for solving classical (historical) ciphers are based on global optimization (meta-heuristic methods). Despite the fact that global optimization is a well-studied problem, in the case of classical ciphers, there are still many open questions such as the construction of fitness functions or efficient transformation of the cryptanalysis (breaking attempt) to an optimization problem. Therefore the transformation of a cryptanalytical task to an optimization problem and the choice of a suitable fitness function form an important part of the topic. In this paper, we focus on the simple columnar transposition in depth. Our main contribution is a detailed analysis and comparison of different fitness functions, fitness landscape analysis and solving experiments.

scholarly journals LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Seyedeh Maedeh Mirmohseni ◽  
Amir Javadpour ◽  
Chunming Tang
Keyword(s):  
Load Balancing ◽  
Optimization Problem ◽  
Fitness Function ◽  
Particle Swarm ◽  
Reducing Power ◽  
Initial Population ◽  
Genetic Optimization ◽  
Cloud Network ◽  
Better Than ◽  
Genetic Optimization Algorithm

Due to the purpose of this study that reducing power consumption in the cloud network is based on load balancing, the fitness function measures the load balance between cloud network and servers (the hosts). This technique is appropriate for handling the resource optimization challenges, due to the ability to convert the load balancing problem into an optimization problem (reducing imbalance cost). In this research, combining the results of the particle swarm genetic optimization (PSGO) algorithm and using a combination of advantages of these two algorithms lead to the improvement of the results and introducing a suitable solution for load balancing operation, because in the proposed approach (LBPSGORA), instead of randomly assigning the initial population in the genetic algorithm, the best result is procured by putting the initial population. The LBPSGORA method is compared with PSO, GA, and hybrid GA-PSO. The execution cost, load balancing, and makespan have been evaluated and our method has performed better than similar methods.

scholarly journals Optimization of Resource Control for Transitions in Complex Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Florin Pop
Keyword(s):  
Complex Systems ◽  
Distributed System ◽  
Resource Selection ◽  
Large Scale ◽  
Optimization Problem ◽  
Fitness Function ◽  
Industrial Process ◽  
Optimal Solution ◽  
Resource Control ◽  
Open Issue

In complex systems like Large-Scale Distributed Systems (LSDSs) the optimization of resource control is an open issue. The large number of resources and multicriteria optimization requirements make the optimization problem a complex one. The importance of resource control increases with the need of use for industrial process and manufacturing, being a key solution for QoS assuring. This paper presents different solutions for multiobjective decentralized control models for tasks assignment in LSDS. The transaction in real-time complex system is modeled in simulation by tasks which will be scheduled and executed in a distributed system, so a set of specifications and requirements are known. The paper presents a critical analysis of existing solutions and focuses on a genetic-based algorithm for optimization. The contribution of the algorithm is the fitness function that includes multiobjective criteria for optimization in different way. Several experimental scenarios, modeled using simulation, were considered to offer a support for analysis of near-optimal solution for resource selection.

scholarly journals Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions

2017 ◽  
Author(s):  
Jingwei Chen ◽  
Robert C. Holte ◽  
Sandra Zilles ◽  
Nathan R. Sturtevant
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Algorithms ◽  
Worst Case ◽  
Heuristic Search Algorithm ◽  
Bidirectional Search ◽  
Optimal Node ◽  
Minimum Number ◽  
Better Than

It is well-known that any admissible unidirectional heuristic search algorithm must expand all states whose f-value is smaller than the optimal solution cost when using a consistent heuristic. Such states are called “surely expanded” (s.e.). A recent study characterized s.e. pairs of states for bidirectional search with consistent heuristics: if a pair of states is s.e. then at least one of the two states must be expanded. This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions. We further prove that no admissible front-to-end algorithm has a worst case better than 2VC. Experimental results show that NBS competes with or outperforms existing bidirectional search algorithms, and often outperforms A* as well.

A new algorithm for finding CRM-model coefficients

2019 ◽  
Vol 5 (3) ◽  
pp. 164-185 ◽  
Author(s):  
Alexander D. Bekman ◽  
Sergey V. Stepanov ◽  
Alexander A. Ruchkin ◽  
Dmitry V. Zelenin
Keyword(s):  
Approximate Solution ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Complex Form ◽  
Approximate Solutions ◽  
The Other ◽  
Programming Algorithm ◽  
Stochastic Algorithms ◽  
Large Set ◽  
Flow Rates

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example&nbsp;— the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

scholarly journals Algebraic Solution to Constrained Bi-Criteria Decision Problem of Rating Alternatives through Pairwise Comparisons

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 303
Author(s):  
Nikolai Krivulin
Keyword(s):  
Decision Problem ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Sufficient Conditions ◽  
Approximation Error ◽  
Optimal Solution ◽  
Algebraic Solution ◽  
Pairwise Comparisons ◽  
Logarithmic Scale ◽  
Idempotent Algebra

We consider a decision-making problem to evaluate absolute ratings of alternatives from the results of their pairwise comparisons according to two criteria, subject to constraints on the ratings. We formulate the problem as a bi-objective optimization problem of constrained matrix approximation in the Chebyshev sense in logarithmic scale. The problem is to approximate the pairwise comparison matrices for each criterion simultaneously by a common consistent matrix of unit rank, which determines the vector of ratings. We represent and solve the optimization problem in the framework of tropical (idempotent) algebra, which deals with the theory and applications of idempotent semirings and semifields. The solution involves the introduction of two parameters that represent the minimum values of approximation error for each matrix and thereby describe the Pareto frontier for the bi-objective problem. The optimization problem then reduces to a parametrized vector inequality. The necessary and sufficient conditions for solutions of the inequality serve to derive the Pareto frontier for the problem. All solutions of the inequality, which correspond to the Pareto frontier, are taken as a complete Pareto-optimal solution to the problem. We apply these results to the decision problem of interest and present illustrative examples.

scholarly journals Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Serena Wang ◽  
Maya Gupta ◽  
Seungil You
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Combinatorial Optimization Problem ◽  
Joint Optimization ◽  
Early Stopping ◽  
Time Approximation ◽  
Approximation Bound ◽  
Evaluation Time ◽  
Speed Up ◽  
Real World Problems

Given a classifier ensemble and a dataset, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble is evaluated. Dynamically deciding to classify early can reduce both mean latency and CPU without harming the accuracy of the original ensemble. To achieve such gains, we propose jointly optimizing the evaluation order of the base models and early-stopping thresholds. Our proposed objective is a combinatorial optimization problem, but we provide a greedy algorithm that achieves a 4-approximation of the optimal solution under certain assumptions, which is also the best achievable polynomial-time approximation bound. Experiments on benchmark and real-world problems show that the proposed Quit When You Can (QWYC) algorithm can speed up average evaluation time by 1.8–2.7 times on even jointly trained ensembles, which are more difficult to speed up than independently or sequentially trained ensembles. QWYC’s joint optimization of ordering and thresholds also performed better in experiments than previous fixed orderings, including gradient boosted trees’ ordering.

Beyond Interactive Evolution: Expressing Intentions Through Fitness Functions

Leonardo ◽  
2016 ◽  
Vol 49 (3) ◽  
pp. 251-256 ◽  
Author(s):  
Penousal Machado ◽  
Tiago Martins ◽  
Hugo Amaro ◽  
Pedro H. Abreu
Keyword(s):  
Fitness Function ◽  
Interactive Art ◽  
Support Tool ◽  
Generative Art ◽  
Fitness Functions ◽  
Meta Level ◽  
Evolutionary Art ◽  
Creativity Support ◽  
The Creation ◽  
The Impact

Photogrowth is a creativity support tool for the creation of nonphotorealistic renderings of images. The authors discuss its evolution from a generative art application to an interactive evolutionary art tool and finally into a meta-level interactive art system in which users express their artistic intentions through the design of a fitness function. The authors explore the impact of these changes on the sense of authorship, highlighting the range of imagery that can be produced by the system.

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