Search performance analysis of qubit convergence measure for quantum-inspired evolutionary algorithm introducing on maximum cut problem

2018 ◽  
Vol 7 (3/4) ◽  
pp. 231
Author(s):  
Yoshifumi Moriyama ◽  
Ichiro Iimura ◽  
Shigeru Nakayama
Keyword(s):  
Performance Analysis ◽  
Evolutionary Algorithm ◽  
Search Performance ◽  
Maximum Cut

Evolutionary Algorithms

2016 ◽  
pp. 190-215 ◽  
Author(s):  
Ka-Chun Wong
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Real World ◽  
Fitness Function ◽  
Search Performance ◽  
Parallel Search ◽  
Survival Selection ◽  
Parent Selection ◽  
History Of ◽  
Search Capability

Inspired from nature, evolutionary algorithms have been proven effective and unique in different real world applications. Comparing to traditional algorithms, its parallel search capability and stochastic nature enable it to excel in search performance in a unique way. In this chapter, evolutionary algorithms are reviewed and discussed from concepts and designs to applications in bioinformatics. The history of evolutionary algorithms is first discussed at the beginning. An overview on the state-of-the-art evolutionary algorithm concepts is then provided. Following that, the related design and implementation details are discussed on different aspects: representation, parent selection, reproductive operators, survival selection, and fitness function. At the end of this chapter, real world evolutionary algorithm applications in bioinformatics are reviewed and discussed.

Game-theoretic Evolutionary Algorithm Based on Behavioral Expectation and its Performance Analysis

2017 ◽  
Vol 31 (5-6) ◽  
pp. 493-517
Author(s):  
Guanci Yang ◽  
Weihua Sheng ◽  
Shaobo Li ◽  
Yang Wang ◽  
Fei Xu
Keyword(s):  
Performance Analysis ◽  
Evolutionary Algorithm ◽  
Game Theoretic

scholarly journals Performance Analysis of Quantum-Inspired Evolutionary Algorithm

2011 ◽  
Vol 15 (8) ◽  
pp. 1095-1102 ◽  
Author(s):  
Tomohisa Takata ◽  
◽  
Teijiro Isokawa ◽  
Nobuyuki Matsui
Keyword(s):  
Quantum Mechanics ◽  
Performance Analysis ◽  
Evolutionary Algorithm ◽  
Knapsack Problem ◽  
Environmental Changes ◽  
Selection Process ◽  
Benchmark Problems ◽  
Search Problem ◽  
Quantum Bits ◽  
Phase Rotation

Quantum-Inspired Evolutionary Algorithm (QEA) is an extension of evolutionary algorithm in which quantum mechanics and its representations are introduced. A chromosome in QEA is represented as a series of qubits (quantum bits), and phase-rotation gates are embedded into the selection process over generations. This algorithm has been shown to have better performances than the classical ones in small benchmark problems, but this has not yet been applied to larger scale problems. We show the performances of this QEA by solving the Knapsack problem, maximum search problem, and construction of image filter. We also investigate the diversity of individuals in a population in order to estimate the robustness against environmental changes.

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