scholarly journals Evolutionary Algorithm with Roulette-Tournament Selection for Solving Aquaculture Diet Formulation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Rosshairy Abd Rahman ◽  
Razamin Ramli ◽  
Zainoddin Jamari ◽  
Ku Ruhana Ku-Mahamud
Keyword(s):  
Evolutionary Algorithm ◽  
The Other ◽  
Roulette Wheel ◽  
Diet Formulation ◽  
Tournament Selection ◽  
Selection Operators ◽  
Selection For ◽  
Feasible Solutions ◽  
Selection Operator ◽  
Formulation Problem

The function of operators in an evolutionary algorithm (EA) is very crucial as the operators have a strong effect on the performance of the EA. In this paper, a new selection operator is introduced for a real valued encoding problem, which specifically exists in a shrimp diet formulation problem. This newly developed selection operator is a hybrid between two well-known established selection operators: roulette wheel and binary tournament selection. A comparison of the performance of the proposed operator and the other existing operator was made for evaluation purposes. The result shows that the proposed roulette-tournament selection is better in terms of its ability to provide many good feasible solutions when a population size of 30 is used. Thus, the proposed roulette-tournament is suitable and comparable to established selection for solving a real valued shrimp diet formulation problem. The selection operator can also be generalized to any problems related to EA.

scholarly journals The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ismael Jannoud ◽  
Yousef Jaradat ◽  
Mohammad Z. Masoud ◽  
Ahmad Manasrah ◽  
Mohammad Alia
Keyword(s):  
Genetic Algorithm ◽  
Extension Problem ◽  
Genetic Operators ◽  
Network Stability ◽  
Stability Period ◽  
Truncation Selection ◽  
Roulette Wheel ◽  
Parent Selection ◽  
Selection Operators ◽  
Selection Operator

A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by 25.8% and data throughput by 26.86%. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

Adaptively Allocating Constraint-Handling Techniques for Constrained Multi-objective Optimization Problems

2021 ◽  
pp. 2159032
Author(s):  
Ning Yang ◽  
Hai-Lin Liu
Keyword(s):  
Optimization Problems ◽  
Performance Measure ◽  
Experimental Results ◽  
The Other ◽  
Constraint Handling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Selection Operators ◽  
Handling Technique ◽  
Feasible Solutions

For solving constrained multi-objective optimization problems (CMOPs), an effective constraint-handling technique (CHT) is of great importance. Recently, many CHTs have been proposed for solving CMOPs. However, no single CHT can outperform all kinds of CMOPs. This paper proposes an algorithm, namely, ACHT-M2M, which adaptively allocates the existing CHTs in an M2M framework for solving CMOPs. To be more specific, a CMOP is first decomposed into several constrained multi-objective optimization subproblems by ACHT-M2M. Each subproblem has a subpopulation in a subregion. CHT for each subregion is adaptively allocated according to a proposed composite performance measure. Population for the next generation is selected from subregions by selection operators with different CHTs and the obtained nondominated feasible solutions in each generation are used to update a predefined archive. ACHT-M2M assembles the advantages of different CHTs and makes them cooperate with each other. The proposed ACHT-M2M is finally compared with the other 12 representative algorithms on benchmark CMOPs and the experimental results further confirm the effectiveness of ACHT-M2M for solving CMOPs.

Placement of Digital Microfluidic Biochips via a New Evolutionary Algorithm

2021 ◽  
Vol 26 (6) ◽  
pp. 1-22
Author(s):  
Chen Jiang ◽  
Bo Yuan ◽  
Tsung-Yi Ho ◽  
Xin Yao
Keyword(s):  
Evolutionary Algorithm ◽  
Resource Constraints ◽  
Scheduling Algorithm ◽  
Search Space ◽  
Placement Problem ◽  
Heuristic Scheduling ◽  
Microfluidic Biochips ◽  
Feasible Solutions ◽  
Digital Microfluidic ◽  
Operation Scheduling

Digital microfluidic biochips (DMFBs) have been a revolutionary platform for automating and miniaturizing laboratory procedures with the advantages of flexibility and reconfigurability. The placement problem is one of the most challenging issues in the design automation of DMFBs. It contains three interacting NP-hard sub-problems: resource binding, operation scheduling, and module placement. Besides, during the optimization of placement, complex constraints must be satisfied to guarantee feasible solutions, such as precedence constraints, storage constraints, and resource constraints. In this article, a new placement method for DMFB is proposed based on an evolutionary algorithm with novel heuristic-based decoding strategies for both operation scheduling and module placement. Specifically, instead of using the previous list scheduler and path scheduler for decoding operation scheduling chromosomes, we introduce a new heuristic scheduling algorithm (called order scheduler) with fewer limitations on the search space for operation scheduling solutions. Besides, a new 3D placer that combines both scheduling and placement is proposed where the usage of the microfluidic array over time in the chip is recorded flexibly, which is able to represent more feasible solutions for module placement. Compared with the state-of-the-art placement methods (T-tree and 3D-DDM), the experimental results demonstrate the superiority of the proposed method based on several real-world bioassay benchmarks. The proposed method can find the optimal results with the minimum assay completion time for all test cases.

The effect of exposure to conspecifics on restlessness in the parasitoid wasp Nasonia vitripennis (Hymenoptera: Pteromalidae)

2007 ◽  
Vol 139 (5) ◽  
pp. 678-684 ◽  
Author(s):  
B.H. King
Keyword(s):  
Habitat Quality ◽  
Parasitoid Wasp ◽  
Poor Quality ◽  
The Other ◽  
Dispersal Ability ◽  
Nasonia Vitripennis ◽  
Adult Females ◽  
Selection For ◽  
The Difference ◽  
Abundance And Distribution

AbstractWhen habitat quality is variable, there should be strong selection for the ability to detect and respond to the variation. Adult females of the parasitoid wasp Nasonia vitripennis (Walker) are known to increase their restlessness (the proportion of time in locomotion) both during and after exposure to a poor quality host. Doing so provides a mechanism for leaving a poor host and potentially finding a better host. This study examined whether restlessness also changes in response to competition as indicated by the presence of adult conspecifics. Both restlessness and the probability of dispersing across an inhospitable environment were greater when a female was with another female than when she was alone. However, restlessness did not remain elevated after the other female was removed. In contrast with females, restlessness of males did not increase either during or after exposure to other males, and the probability of dispersing across an inhospitable environment was unaffected by the presence of another male. The difference between females and males may be related to differences in dispersal ability and in the abundance and distribution of hosts versus mates.

scholarly journals Evaluation of Resistance Development in Bemisia tabaci Genn. (Homoptera: Aleyrodidae) in Cotton against Different Insecticides

Insects ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 996
Author(s):  
Muhammad Zaryab Khalid ◽  
Sohail Ahmed ◽  
Ibrahim Al-ashkar ◽  
Ayman EL Sabagh ◽  
Liyun Liu ◽  
...  
Keyword(s):  
Bemisia Tabaci ◽  
The Other ◽  
Resistance Development ◽  
Susceptible Population ◽  
Low Level ◽  
Development Of Resistance ◽  
Major Pest ◽  
Selection For ◽  
High Level ◽  
Very High

Cotton is a major crop of Pakistan, and Bemisia tabaci (Homoptera: Aleyrodidae) is a major pest of cotton. Due to the unwise and indiscriminate use of insecticides, resistance develops more readily in the whitefly. The present study was conducted to evaluate the resistance development in the whitefly against the different insecticides that are still in use. For this purpose, the whitefly population was selected with five concentrations of each insecticide, for five generations. At G1, compared with the laboratory susceptible population, a very low level of resistance was observed against bifenthrin, cypermethrin, acetamiprid, imidacloprid, thiamethoxam, nitenpyram, chlorfenapyr, and buprofezin with a resistance ratio of 3-fold, 2-fold, 1-fold, 4-fold, 3-fold, 3-fold, 3-fold, and 3-fold, respectively. However, the selection for five generations increased the resistance to a very high level against buprofezin (127-fold), and to a high level against imidacloprid (86-fold) compared with the laboratory susceptible population. While, a moderate level of resistance was observed against cypermethrin (34-fold), thiamethoxam (34-fold), nitenpyram (30-fold), chlorfenapyr (29-fold), and acetamiprid (21-fold). On the other hand, the resistance was low against bifenthrin (18-fold) after selection for five generations. A very low level of resistance against the field population of B. tabaci, at G1, showed that these insecticides are still effective, and thus can be used under the field conditions for the management of B. tabaci. However, the proper rotation of insecticides among different groups can help to reduce the development of resistance against insecticides.

scholarly journals Development of bioactive/active additives based on essential oils: A review

2020 ◽  
Vol 3 (4) ◽  
pp. 60-67
Author(s):  
Jessica Alarcón-Moyano ◽  
◽  
Silvia Matiacevich ◽  
Keyword(s):  
Medicinal Plants ◽  
Essential Oil ◽  
Essential Oils ◽  
Freeze Drying ◽  
The Other ◽  
Wall Material ◽  
Oil Oxidation ◽  
Active Additive ◽  
Wall Materials ◽  
Selection For

The use of bioactive/active additives has been increasing in recent years, especially those derived from medicinal plants such as essential oils. However, due to essential oil oxidation it is necessary to protect it by encapsulation techniques such as: emulsion, spray- and/or freeze-drying as the most economical techniques. On the other hand, an important factor is to determine the appropriate wall material to obtain a prolonged or controlled release in the food or in the organism. Therefore, several factors affect the release of the compounds such as the type, amount of wall material and/or combination of wall materials. Therefore, the knowledge of all the aforementioned factors is important in order to make an adequate selection for the development of a bioactive/active additive based on essential oils.

scholarly journals Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

2017 ◽  
Vol 42 (4) ◽  
pp. 339-358 ◽  
Author(s):  
Krzysztof Krawiec ◽  
Paweł Liskowski
Keyword(s):  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Matrix Factorization ◽  
Fitness Function ◽  
Interaction Matrix ◽  
Simulated Evolution ◽  
Adaptive Test ◽  
Selection For ◽  
Correct Program ◽  
Non Negative Matrix Factorization

Abstract Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simulated evolution are computer programs. A fitness function in GP is usually based on a set of tests, each of which defines the desired output a correct program should return for an exemplary input. The outcomes of interactions between programs and tests in GP can be represented as an interaction matrix, with rows corresponding to programs in the current population and columns corresponding to tests. In previous work, we proposed SFIMX, a method that performs only a fraction of interactions and employs non-negative matrix factorization to estimate the outcomes of remaining ones, shortening GP’s runtime. In this paper, we build upon that work and propose three extensions of SFIMX, in which the subset of tests drawn to perform interactions is selected with respect to test difficulty. The conducted experiment indicates that the proposed extensions surpass the original SFIMX on a suite of discrete GP benchmarks.

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