scholarly journals Benchmarking a $$(\mu +\lambda )$$ Genetic Algorithm with Configurable Crossover Probability

2020 ◽  
pp. 699-713
Author(s):  
Furong Ye ◽  
Hao Wang ◽  
Carola Doerr ◽  
Thomas Bäck
Keyword(s):  
Genetic Algorithm ◽  
Crossover Probability

Research on the Optimization of the Numerical Value Based on Improved Genetic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1256-1260
Author(s):  
Zhen Dong Li ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Adaptive Genetic Algorithms ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability ◽  
New Algorithms ◽  
Relevance Index

For the lack of crossover operation, from three aspects of crossover operation , systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithms global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithms convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.

scholarly journals The quasispecies regime for the simple genetic algorithm with roulette wheel selection

2017 ◽  
Vol 49 (3) ◽  
pp. 903-926 ◽  
Author(s):  
Raphaël Cerf
Keyword(s):  
Genetic Algorithm ◽  
Selection Mechanism ◽  
Roulette Wheel Selection ◽  
Mutation Probability ◽  
Roulette Wheel ◽  
Simple Genetic Algorithm ◽  
Crossover Probability ◽  
Mean Fitness ◽  
The Mean ◽  
Best Fit

Abstract We introduce a new parameter to discuss the behavior of a genetic algorithm. This parameter is the mean number of exact copies of the best-fit chromosomes from one generation to the next. We believe that the genetic algorithm operates best when this parameter is slightly larger than 1 and we prove two results supporting this belief. We consider the case of the simple genetic algorithm with the roulette wheel selection mechanism. We denote by ℓ the length of the chromosomes, m the population size, pC the crossover probability, and pM the mutation probability. Our results suggest that the mutation and crossover probabilities should be tuned so that, at each generation, the maximal fitness multiplied by (1 - pC)(1 - pM)ℓ is greater than the mean fitness.

Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

2014 ◽  
Vol 951 ◽  
pp. 274-277 ◽  
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Design ◽  
Rbf Neural Network ◽  
Hybrid Genetic Algorithm ◽  
Proposed Model ◽  
Crossover Probability ◽  
Ga Algorithm ◽  
The Hierarchical Structure ◽  
Adaptive Crossover

To improve the optimization design of Radial Basis Function (RBF) neural network, a RBF neural network based on a hybrid Genetic Algorithm (GA) is proposed. First the hierarchical structure and adaptive crossover probability is introduced into the traditional GA algorithm for the improvement, and then the hybrid GA algorithm is used to optimize the structure and parameters of the network. The simulation indicates that the proposed model has a good modeling performance.

scholarly journals An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Yunqing Rao ◽  
Dezhong Qi ◽  
Jinling Li
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Convergence Rates ◽  
Cutting Stock ◽  
Hierarchical Coding ◽  
Crossover Probability ◽  
Speed Up ◽  
Adaptive Crossover ◽  
Process Constraints ◽  
Hierarchical Genetic Algorithm

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involvesncutting patterns formnon-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

scholarly journals Penentuan Jarak Terpendek Pada Jalur Pengiriman Musae Chips Dengan Menggunakan Algoritma Genetika

2019 ◽  
Vol 6 (1) ◽  
pp. 19-23
Author(s):  
Muhammad Bahrul Arif
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Distribution Channel ◽  
Optimal Solutions ◽  
Distribution Lines ◽  
Good Path ◽  
Crossover Probability ◽  
Study Results ◽  
Genetic Algorithm Method

Combination of good path distribution by land can optimize travel time and costs. However, not all of these path distribution combinations will provide the best solution. The study was conducted to determine the distribution of goods so that the best solution is achieved. To simplify the process of determining the goods distribution channel, it is supported by software development. Genetic algorithms that have reliability in producing optimal solutions can be used to solve this problem. The application of the genetic algorithm method is applied in software. In the software that is built, there are several inputs needed, namely: cities destination distribution as the initial chromosome number, number of generations, crossover probability and probability of mutation. The result of processing is a combination of goods distribution lines to be taken which represent the solution to this problem. Only the best chromosomes will be given as a result. Through the software that was built, the determination of goods distribution lines is expected to be better and can optimize the time and cost of travel. Based on the research, 5 road combinations are used as chip distribution routes used for testing. This study results from the first fitness gene get the highest fitness 7.4, fitness lowest 5.6 and the second gene get the highest fitness 9.3, fitness lowest 5.6.

scholarly journals PENERAPAN TEKNIK KLASIFIKASI PADA SISTEM REKOMENDASI MENGGUNAKAN ALGORITMA GENETIKA

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Rita Rismala ◽  
Mahmud Dwi Sulistiyo
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Recommendation System ◽  
Classification Model ◽  
Mutation Probability ◽  
Linear Classifier ◽  
Average Accuracy ◽  
Crossover Probability ◽  
Best Parameter

[Id]Sistem rekomendasi yang dibangun dalam penelitian ini adalah sistem rekomendasi yang dapat memberikan rekomendasi sebuah item terbaik kepada user. Dari sisi data mining, pembangunan sistem rekomendasi satu item ini dapat dipandang sebagai upaya untuk membangun sebuah model classifier yang dapat digunakan untuk mengelompokkan data ke dalam satu kelas tertentu. Model classifier yang digunakan bersifat linier. Untuk menghasilkan konfigurasi model classifier yang optimal digunakan Algoritma Genetika (AG). Performansi AG dalam melakukan optimasi pada model klasifikasi linier yang digunakan cukup dapat diterima. Untuk dataset yang digunakan dengan kombinasi nilai parameter terbaik yaitu yaitu ukuran populasi 50, probabilitas crossover 0.7, dan probabilitas mutasi 0.1, diperoleh rata-rata akurasi sebesar 72.80% dengan rata-rata waktu proses 6.04 detik, sehingga penerapan teknik klasifikasi menggunakan AG dapat menjadi solusi alternatif dalam membangun sebuah sistem rekomendasi, namun dengan tetap memperhatikan pengaturan nilai parameter yang sesuai dengan permasalahan yang dihadapi.Kata kunci:sistem rekomendasi, klasifikasi, Algoritma Genetika[En]In this study was developed a recommendation system that can recommend top-one item to a user. In terms of data mining, it can be seen as a problem to develop a classifier model that can be used to classify data into one particular class. The model used was a linear classifier. To produce the optimal configuration of classifier model was used Genetic Algorithm (GA). GA performance in optimizing the linear classification model was acceptable. Using the case study dataset and combination of the best parameter value, namely population size 50, crossover probability 0.7 and mutation probability 0.1, obtained average accuracy 72.80% and average processing time of 6.04 seconds, so that the implementation of classification techniques using GA can be an alternative solution in developing a recommender system, due regard to setting the parameter value depend on the encountered problem.Keywords:Recommendation system, classification, Genetic Algorithm

scholarly journals Effect of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines for BI- Criteria Objectives

2019 ◽  
Vol 9 (1) ◽  
pp. 2827-2831
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Parallel Machines ◽  
Optimization Techniques ◽  
Computational Time ◽  
Planning And Scheduling ◽  
Multi Objective ◽  
Mutation Operators ◽  
Crossover Probability ◽  
Wide Range

Optimization of multi objective function gain the importance in the scheduling process. Many classical techniques are available to address the multi objective functions but the solutions yield the unsatisfactory results when the problem becomes complex and large. Evolutionary algorithm would be the solution for such problems. Genetic algorithm is adaptive heuristic search algorithms and optimization techniques that mimic the process of natural evolution. Genetic algorithms are a very effective way of obtaining a reasonable solution quickly to a complex problem. The genetic algorithm operators such as selection method, crossover method, crossover probability, mutation operators and stopping criteria have an effect on obtaining the reasonably good solution and the computational time. Partially mapped crossover operators are used to solve the problem of the traveling salesman, planning and scheduling of the machines, etc., which are having a wide range of solutions. This paper presents the effect of crossover probability on the performance of the genetic algorithm for the bi-criteria objective function to obtain the best solution in a reasonable time. The simulation on a designed genetic algorithm was conducted with a crossover probability of 0.4 to 0.95 (with a step of 0.05) and 0.97, found that results were converging for the crossover probability of 0.6 with the computational time of 3.41 seconds.

The Application of an Improved Genetic Algorithm to Signal Acquisition

2014 ◽  
Vol 945-949 ◽  
pp. 2319-2322
Author(s):  
Min Shi ◽  
Cong Cong Tian ◽  
Qing Ming Yi ◽  
Song Li
Keyword(s):  
Genetic Algorithm ◽  
Initial Population ◽  
Signal Acquisition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Signal Parameters ◽  
Crossover Probability ◽  
Dynamic Search ◽  
Gps Signal Acquisition ◽  
Adaptive Crossover

The paper proposes an improved genetic algorithm which is applied to the problem of GPS signal acquisition.The improved genetic algorithm uses the small section method of the initial population, dynamic search range of parameter, and the adaptive crossover probability and mutation probability to search GPS signal parameters. Simulations and experiment results show that the improved genetic algorithm can search the signal parameters rapidly and precisely. The GPS signal acquisition performance is improved.

scholarly journals Optimasi Biaya Distribusi Rantai Pasok Tiga Tingkat dengan Menggunakan Algoritma Genetika Adaptif dan Terdistribusi

2014 ◽  
Vol 10 (1) ◽  
pp. 189
Author(s):  
Zulfahmi Indra ◽  
Subanar Subanar
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Migration Rate ◽  
Adaptive Genetic Algorithm ◽  
Chain Management ◽  
Stepping Stone ◽  
Mutation Probability ◽  
Crossover Probability ◽  
Distributed Genetic Algorithm

AbstrakManajemen rantai pasok merupakan hal yang penting. Inti utama dari manajemen rantai pasok adalah proses distribusi. Salah satu permasalahan distribusi adalah strategi keputusan dalam menentukan pengalokasian banyaknya produk yang harus dipindahkan mulai dari tingkat manufaktur hingga ke tingkat pelanggan. Penelitian ini melakukan optimasi rantai pasok tiga tingkat mulai dari manufaktur-distributor-gosir-retail. Adapun pendekatan yang dilakukan adalah algoritma genetika adaptif dan terdistribusi. Solusi berupa alokasi banyaknya produk yang dikirim pada setiap tingkat akan dimodelkan sebagai sebuah kromosom. Parameter genetika seperti jumlah kromosom dalam populasi, probabilitas crossover dan probabilitas mutasi akan secara adaptif berubah sesuai dengan kondisi populasi pada generasi tersebut. Dalam penelitian ini digunakan 3 sub populasi yang bisa melakukan pertukaran individu setiap saat sesuai dengan probabilitas migrasi. Adapun hasil penelitian yang dilakukan 30 kali untuk setiap perpaduan nilai parameter genetika menunjukkan bahwa nilai biaya terendah yang didapatkan adalah 80,910, yang terjadi pada probabilitas crossover 0.4, probabilitas mutasi 0.1, probabilitas migrasi 0.1 dan migration rate 0.1. Hasil yang diperoleh lebih baik daripada metode stepping stone yang mendapatkan biaya sebesar 89,825. Kata kunci— manajemen rantai pasok, rantai pasok tiga tingkat, algortima genetika adaptif, algoritma genetika terdistribusi. Abstract Supply chain management is critical in business area. The main core of supply chain management is the process of distribution. One issue is the distribution of decision strategies in determining the allocation of the number of products that must be moved from the level of the manufacture to the customer level. This study take optimization of three levels distribution from manufacture-distributor-wholeshale-retailer. The approach taken is adaptive and distributed genetic algorithm. Solution in the form of allocation of the number of products delivered at each level will be modeled as a chromosome. Genetic parameters such as the number of chromosomes in the population, crossover probability and adaptive mutation probability will change adaptively according to conditions on the population of that generation. This study used 3 sub-populations that exchange individuals at any time in accordance with the probability of migration. The results of research conducted 30 times for each value of the parameter genetic fusion showed that the lowest cost value obtained is 80,910, which occurs at the crossover probability 0.4, mutation probability 0.1, the probability of migration 0.1 and migration rate 0.1. This result has shown that adaptive and distributed genetic algorithm is better than stepping stone method that obtained 89,825. Keywords— management supply chain, three level supply chain, adaptive genetic algorithm, distributed genetic algorithm.

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