Co-Evolutionary Algorithms Based on Mixed Strategy

2013 ◽  
pp. 75-88
Author(s):  
Wei Hou ◽  
HongBin Dong ◽  
GuiSheng Yin
Keyword(s):  
Evolutionary Algorithms ◽  
Mixed Strategy ◽  
Single Point ◽  
Evolutionary Game ◽  
Benchmark Problems ◽  
The Novel ◽  
Mutation Operators ◽  
Mutation Strategy ◽  
Novel Algorithms ◽  
Significant Superiority

Inspired by evolutionary game theory, this paper modifies previous mixed strategy framework, adding a new mutation operator and extending to crossover operation, and proposes co-evolutionary algorithms based on mixed crossover and/or mutation strategy. The mixed mutation strategy set consists of Gaussian, Cauchy, Levy, single point and differential mutation operators; the mixed crossover strategy set consists of cuboid, two-points and heuristic crossover operators. The novel algorithms automatically select crossover and/or mutation operators from a given mixed strategy set, and improve the evolutionary performance by dynamically utilizing the most effective operator at different stages of evolution. The proposed algorithms are tested on a set of 21 benchmark problems. The results show that the new mixed strategies perform equally well or better than the best of the previous evolutionary methods for all of the benchmark problems. The proposed MMCGA has shown significant superiority over others.

Co-Evolutionary Algorithms Based on Mixed Strategy

2011 ◽  
Vol 4 (2) ◽  
pp. 17-30
Author(s):  
Wei Hou ◽  
HongBin Dong ◽  
GuiSheng Yin
Keyword(s):  
Evolutionary Algorithms ◽  
Mixed Strategy ◽  
Single Point ◽  
Evolutionary Game ◽  
Benchmark Problems ◽  
The Novel ◽  
Mutation Operators ◽  
Mutation Strategy ◽  
Novel Algorithms ◽  
Significant Superiority

Inspired by evolutionary game theory, this paper modifies previous mixed strategy framework, adding a new mutation operator and extending to crossover operation, and proposes co-evolutionary algorithms based on mixed crossover and/or mutation strategy. The mixed mutation strategy set consists of Gaussian, Cauchy, Levy, single point and differential mutation operators; the mixed crossover strategy set consists of cuboid, two-points and heuristic crossover operators. The novel algorithms automatically select crossover and/or mutation operators from a given mixed strategy set, and improve the evolutionary performance by dynamically utilizing the most effective operator at different stages of evolution. The proposed algorithms are tested on a set of 21 benchmark problems. The results show that the new mixed strategies perform equally well or better than the best of the previous evolutionary methods for all of the benchmark problems. The proposed MMCGA has shown significant superiority over others.

scholarly journals Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Libin Hong ◽  
Chenjian Liu ◽  
Jiadong Cui ◽  
Fuchang Liu
Keyword(s):  
Survival Rate ◽  
Novel Mutation ◽  
Single Point ◽  
Evolutionary Process ◽  
Evolutionary Programming ◽  
Adaptive Mutation ◽  
Single Point Mutation ◽  
Step Size ◽  
Mutation Operators ◽  
Mutation Strategy

Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limitations and does not display outstanding performance on some benchmark functions. In this work, we proposed a novel mutation strategy based on both the “step size” and “survival rate” for EP (SSMSEP). SSMSEP-1 and SSMSEP-2 are two variants of SSMSEP, which use “survival rate” or “step size” separately. Our proposed method can select appropriate mutation operators and update parameters for mutation operators according to diverse landscapes during the evolutionary process. Compared with SSMSEP-1, SSMSEP-2, SSEP, and other EP variants, the SSMSEP demonstrates its robustness and stable performance on most benchmark functions tested.

An Essay on the Origins of the Novel

2018 ◽  
Author(s):  
Tim Whitmarsh
Keyword(s):  
Single Point ◽  
The Novel ◽  
History Of ◽  
History Of Scholarship

This chapter discusses the history of scholarship trying to trace the origins of the novel, and the impossibility of attempting to pin down a single point of origination.

scholarly journals Entangled and correlated photon mixed strategy for social decision making

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shion Maeda ◽  
Nicolas Chauvet ◽  
Hayato Saigo ◽  
Hirokazu Hori ◽  
Guillaume Bachelier ◽  
...  
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Mixed Strategy ◽  
Evolutionary Game ◽  
Slot Machines ◽  
Decision Strategy ◽  
Social Infrastructure ◽  
Positive Effects ◽  
Social Decision Making ◽  
The Given

AbstractCollective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of applications such as resource management in social infrastructure. In a previous study, we theoretically and experimentally demonstrated that entangled photons can physically resolve the difficulty of the CMAB problem. This decision-making strategy completely avoids decision conflicts while ensuring equality. However, decision conflicts can sometimes be beneficial if they yield greater rewards than non-conflicting decisions, indicating that greedy actions may provide positive effects depending on the given environment. In this study, we demonstrate a mixed strategy of entangled- and correlated-photon-based decision-making so that total rewards can be enhanced when compared to the entangled-photon-only decision strategy. We show that an optimal mixture of entangled- and correlated-photon-based strategies exists depending on the dynamics of the reward environment as well as the difficulty of the given problem. This study paves the way for utilizing both quantum and classical aspects of photons in a mixed manner for decision making and provides yet another example of the supremacy of mixed strategies known in game theory, especially in evolutionary game theory.

TABU PROGRAMMING: A NEW PROBLEM SOLVER THROUGH ADAPTIVE MEMORY PROGRAMMING OVER TREE DATA STRUCTURES

2011 ◽  
Vol 10 (02) ◽  
pp. 373-406 ◽  
Author(s):  
ABDEL-RAHMAN HEDAR ◽  
EMAD MABROUK ◽  
MASAO FUKUSHIMA
Keyword(s):  
Search Algorithm ◽  
Benchmark Problems ◽  
Problem Solver ◽  
Learning Tools ◽  
Mutation Operators ◽  
Tree Data ◽  
Tree Data Structure ◽  
Comprehensive Framework ◽  
Rate Of Success ◽  
Crossover And Mutation

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favorably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.

The Optimal Design on Connection Order of Network in Hydraulic Manifold Block

2011 ◽  
Vol 201-203 ◽  
pp. 2190-2194
Author(s):  
Jun Jun Zhang ◽  
Ji Sheng Wang ◽  
Jiang Yong Wang ◽  
Gang Liu ◽  
Jie Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Genetic Manipulation ◽  
Optimal Algorithm ◽  
Single Point ◽  
Adaptive Mutation ◽  
Gene Encoding ◽  
Mutation Strategy ◽  
The Common

As one of the important questions in the design of hydraulic manifold block — connection order of network, give a solution based on genetic algorithm. Genetic algorithm is the common effective intelligent optimal algorithm and suitable for solving a large combinatorial optimal problems. Gene encoding of ordinal representation, single-point crossover strategy and adaptive mutation strategy are used in the design of genetic manipulation.

scholarly journals Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness

2020 ◽  
Vol 22 (3) ◽  
pp. 128-139
Author(s):  
Esra'a Alkafaween ◽  
Ahmad B. A. Hassanat
Keyword(s):  
Success Factors ◽  
Optimization Problems ◽  
Combinatorial Problems ◽  
Mutation Operator ◽  
Efficient Tool ◽  
Key Success Factors ◽  
Mutation Operators ◽  
Mutation Strategy ◽  
Symmetric Traveling Salesman Problem ◽  
Hybrid Mutation Operator

Genetic algorithm (GA) is an efficient tool for solving optimization problems by evolving solutions, as it mimics the Darwinian theory of natural evolution. The mutation operator is one of the key success factors in GA, as it is considered the exploration operator of GA. Various mutation operators exist to solve hard combinatorial problems such as the TSP. In this paper, we propose a hybrid mutation operator called "IRGIBNNM", this mutation is a combination of two existing mutations; a knowledgebased mutation, and a random-based mutation. We also improve the existing “select best mutation” strategy using the proposed mutation. We conducted several experiments on twelve benchmark Symmetric traveling salesman problem (STSP) instances. The results of our experiments show the efficiency of the proposed mutation, particularly when we use it with some other mutations.

Principle and Simulation Study on Multi Point-Single Point Incremental Combined Forming for Sheet Metal

2009 ◽  
Vol 626-627 ◽  
pp. 273-278 ◽  
Author(s):  
X.J. Li ◽  
Ming Zhe Li ◽  
C.G. Liu ◽  
Zhong Yr Cai
Keyword(s):  
Sheet Metal ◽  
Single Point ◽  
Experimental Result ◽  
Work Piece ◽  
The Novel ◽  
Forming Process ◽  
Explicit Finite Element ◽  
Forming Defect ◽  
Forming Technology ◽  
Elastic Cushion

Based on Multi-Point (MP) forming technology and Single-Point Incremental (SPI) forming technology, MP-SPI combined forming method for sheet metal is proposed, the principle and two different forming techniques are illustrated firstly. Then the paper is focused on numerical analysis for the novel forming technique with explicit Finite Element (FE) algorithm. During simulation of spherical work-piece, dimpling occurs as a main forming defect in MP-SPI combined forming process. Simulation results show that the dimpling defect can be suppressed effectively by using elastic cushion. An appropriate thickness of elastic cushion is necessary to prevent dimpling. And also the deformation of the work-piece is sensitive to the shape of elastic cushion. The combined forming test shows that the numerical simulation result is closed to the experimental result.

scholarly journals Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-36 ◽  
Author(s):  
Wei Li ◽  
Lei Wang ◽  
Quanzhu Yao ◽  
Qiaoyong Jiang ◽  
Lei Yu ◽  
...  
Keyword(s):  
Solid State ◽  
Differential Evolution ◽  
Liquid State ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Gaseous State ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Cloud Particles

We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE) algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

Knowledge-Intensive Evolutionary Algorithms for Solving a Healthcare Fleet Optimization Problem

2018 ◽  
pp. 192-223
Author(s):  
Carlos Adrian Catania ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Pierre Collet
Keyword(s):  
Evolutionary Algorithms ◽  
Knowledge Engineering ◽  
Domain Ontology ◽  
Real Problem ◽  
Fitness Functions ◽  
Evaluation Functions ◽  
Mutation Operators ◽  
Knowledge Intensive ◽  
Definition Of ◽  
Crossover And Mutation

In this chapter, the authors show how knowledge engineering techniques can be used to guide the definition of evolutionary algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. Various representations of the fitness functions, the genome, and mutation/crossover operators adapted to different types of problems (routing, scheduling, etc.) have been proposed in the literature. However, real problems including specific constraints (legal restrictions, specific usages, etc.) are often overlooked by the proposed generic models. To ensure that these constraints are effectively considered, the authors propose a methodology based on the structuring of the conceptual model underlying the problem, as a labelled domain ontology suitable for optimization by EA. The authors show that a precise definition of the knowledge model with a labelled domain ontology can be used to describe the chromosome, the evaluation functions, and the crossover and mutation operators. The authors show the details for a real implementation and some experimental results.

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