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.

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.

Hereditary Protein C Deficiency Associated with Mutations in Exon IX of the Protein C Gene

1994 ◽  
Vol 72 (02) ◽  
pp. 203-208 ◽  
Author(s):  
R G Doig ◽  
C G Begley ◽  
K M McGrath
Keyword(s):  
Amino Acid ◽  
Protein C ◽  
Novel Mutation ◽  
Single Point ◽  
Enzyme Analysis ◽  
Restriction Enzyme Analysis ◽  
Single Point Mutation ◽  
Nucleotide Position ◽  
Type I ◽  
Protein C Deficiency

SummaryThis report describes five families with symptomatic hereditary protein C deficiency. Using a polymerase chain reaction (PCR)-based method, the entire coding sequence and intron-exon boundaries of the protein C gene was amplified from genomic DNA. In each family a single point mutation in the protein C gene was identified. Two unrelated families were found to share the same mutation, while the other three had different mutations. In the first two families with type I protein C deficiency the normal cytosine residue at nucleotide position 8551 in the protein C gene was replaced by thymidine leading to substitution of the normal proline residue at amino acid position 279 by leucine. In the third family with type I deficiency a previously undescribed mutation was identified. In this family the guanosine residue at position 8559 was replaced by adenosine (glycine 282 substituted by serine). In the fourth family, also with type I deficiency, guanosine 8589 was replaced by adenosine (glycine 292 substituted by serine). The fifth family had type II deficiency and in affected members cytosine 8769 was replaced by thymidine (arginine 352 substituted by tryptophan). All these mutations lead to amino acid substitutions in the serine protease domain of the mature protein. All were able to be confirmed by restriction enzyme analysis of PCR-derived DNA. In addition the novel mutation at nucleotide position 8559 was also demonstrable using single strand conformation polymorphism (SSCP) analysis of PCR-derived DNA. These mutations were likely examples of deamination of methylated cytosine occurring in cytosine-phosphate-guanosine (CpG) dinucleotide sequences. These findings confirm the genetic heterogeneity of hereditary protein C deficiency in these families.

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 CHAOTIC INITIALIZED MULTIPLE OBJECTIVE DIFFERENTIAL EVOLUTION WITH ADAPTIVE MUTATION STRATEGY (CA-MODE) FOR CONSTRUCTION PROJECT TIME-COST-QUALITY TRADE-OFF

2015 ◽  
Vol 22 (2) ◽  
pp. 210-223 ◽  
Author(s):  
Min-Yuan CHENG ◽  
Duc-Hoc TRAN ◽  
Minh-Tu CAO
Keyword(s):  
Differential Evolution ◽  
Evolutionary Process ◽  
Adaptive Mutation ◽  
Multiple Objective ◽  
Initial Population ◽  
Time Cost ◽  
Nsga Ii ◽  
Trade Off ◽  
Mutation Strategy ◽  
Chaos Sequences

Time, cost and quality are three factors playing an important role in the planning and controlling of construc­tion. Trade-off optimization among them is significant for the improvement of the overall benefits of construction pro­jects. In this paper, a novel optimization model, named as Chaotic Initialized Multiple Objective Differential Evolution with Adaptive Mutation Strategy (CA-MODE), is developed to deal with the time-cost-quality trade-off problems. The proposed algorithm utilizes the advantages of chaos sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. A numerical case study of highway construction is used to illustrate the application of CA-MODE. It has been shown that non-dominated solutions generated by CA-MODE assist project managers in choosing appropriate plan which is other­wise hard and time-consuming to obtain. The comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previ­ous results verify the efficiency and effectiveness of the proposed algorithm.

Taboo evolutionary programming: a new method of global optimization

2006 ◽  
Vol 462 (2076) ◽  
pp. 3613-3627 ◽  
Author(s):  
Mingjun Ji ◽  
Jacek Klinowski
Keyword(s):  
Global Optimization ◽  
Global Minimum ◽  
Single Point ◽  
Evolutionary Programming ◽  
Optimization Method ◽  
Efficient Global Optimization ◽  
Benchmark Problems ◽  
Single Point Mutation ◽  
Central Idea ◽  
Real World Problems

We introduce taboo evolutionary programming, a very efficient global optimization method which combines features of single-point mutation evolutionary programming (SPMEP) and taboo search. As demonstrated by solving 18 benchmark problems, the algorithm is not trapped in local minima and quickly approaches the global minimum. The results are superior to those from SPMEP, fast evolutionary programming and generalized evolutionary programming. The method is easily applicable to real-world problems, and the central idea may be introduced into other algorithms.

scholarly journals A single-nucleotide change underlies the genetic assimilation of a plastic trait

2021 ◽  
Vol 7 (6) ◽  
pp. eabd9941
Author(s):  
Paul Vigne ◽  
Clotilde Gimond ◽  
Céline Ferrari ◽  
Anne Vielle ◽  
Johan Hallin ◽  
...  
Keyword(s):  
Evolutionary Biology ◽  
Single Point ◽  
Evolutionary Process ◽  
Resource Provisioning ◽  
Egg Laying ◽  
Genetic Assimilation ◽  
Single Amino Acid ◽  
Single Point Mutation ◽  
Single Amino Acid Change ◽  
Plastic Trait

Genetic assimilation—the evolutionary process by which an environmentally induced phenotype is made constitutive—represents a fundamental concept in evolutionary biology. Thought to reflect adaptive phenotypic plasticity, matricidal hatching in nematodes is triggered by maternal nutrient deprivation to allow for protection or resource provisioning of offspring. Here, we report natural Caenorhabditis elegans populations harboring genetic variants expressing a derived state of near-constitutive matricidal hatching. These variants exhibit a single amino acid change (V530L) in KCNL-1, a small-conductance calcium-activated potassium channel subunit. This gain-of-function mutation causes matricidal hatching by strongly reducing the sensitivity to environmental stimuli triggering egg-laying. We show that reestablishing the canonical KCNL-1 protein in matricidal isolates is sufficient to restore canonical egg-laying. While highly deleterious in constant food environments, KCNL-1 V530L is maintained under fluctuating resource availability. A single point mutation can therefore underlie the genetic assimilation—by either genetic drift or selection—of an ancestrally plastic trait.

scholarly journals Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming

2021 ◽  
Author(s):  
Libin Hong ◽  
John R. Woodward ◽  
Ender Özcan ◽  
Fuchang Liu
Keyword(s):  
Evolutionary Programming ◽  
Search Space ◽  
Heuristic Approach ◽  
Adaptive Mutation ◽  
Mutation Operator ◽  
Adaptive Search ◽  
The Core ◽  
Mutation Operators ◽  
Improved Performance ◽  
Over Time

AbstractGenetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Lévy distribution. This study proposes a hyper-heuristic approach that employs GP to automatically design different mutation operators for EP. At each generation, the EP algorithm can adaptively explore the search space according to historical information. The experimental results demonstrate that the EP with adaptive mutation operators, designed by the proposed hyper-heuristics, exhibits improved performance over other EP versions (both manually and automatically designed). Many researchers in evolutionary computation advocate adaptive search operators (which do adapt over time) over non-adaptive operators (which do not alter over time). The core motive of this study is that we can automatically design adaptive mutation operators that outperform automatically designed non-adaptive mutation operators.

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