Seeking a balance between population diversity and premature convergence for real-coded genetic algorithms with crossover operator

Naqvi, Fakhra Batool; Shad, Muhammad Yousaf

doi:10.1007/s12065-021-00636-4

Cited by 11 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 3: The offsprings are generated and defined in this manner: CEC-2017 benchmark problem set has been selected to test the performance of the proposed real-coded crossover operator. To make a comparison with four existing real-coded crossover operators, namely, Logistic Crossover (Log-X) [35], Exponentiated Pareto Distribution Crossover (EPX) [36], Laplace Crossover (LX) [37], and Weibull Crossover (WX) [38]. These five real-coded crossover operators have been used with the existing real-coded mutation operators, namely, Direction-based Exponential Mutation (DEM) [39], Makinen, Periaux, and Toivanen Mutation (MPTM) [40], and Polynomial Mutation (PLYM) [41].…”

Section: Flexibilitymentioning

confidence: 99%

Installation of Smog-Free Towers Using Novel Real Coded Genetic Algorithm

Kumar,

Deep,

Nagar

2024

IEEE Access

View full text Add to dashboard Cite

The circle packing problem involves finding the best way to place non-overlapping circles within a given space, while the smog-free tower installation problem aims to minimize the exposure of residents to secondhand smog by identifying the optimal tower locations. This study proposes a Real Coded Genetic Algorithm (RCGA) that uses real-valued representations of circle positions to solve the smog-free tower installation problem. A new crossover operator is introduced, combining the information from two parent solutions to generate two new offspring solutions. The operator uses a random crossover point and two scaling factors to control the amount of information exchanged. The performance of the operator is evaluated on CEC-2017 benchmark problem set and compared to other commonly used operators, with results indicating that it produces high-quality solutions and outperforms other operators in terms of solution quality and convergence speed. This research contributes to developing effective optimization algorithms that can have important implications for improving public health and reducing the negative effects of secondhand smog.

show abstract

Section: Flexibilitymentioning

confidence: 99%

Installation of Smog-Free Towers Using Novel Real Coded Genetic Algorithm

Kumar,

Deep,

Nagar

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The optimization literature acknowledging the diversity of population is a vital factor in search of a global optimal solution. This is evident by the discussion on the relevance of premature convergence and population diversity, see, for example, Hussain andMuhammad [13,15]. So it is clear from these studies that the performance of GA is mostly affected by the choice of selection operator.…”

Section: Introductionmentioning

confidence: 96%

Genetic algorithm with a new round-robin based tournament selection: Statistical properties analysis

et al. 2022

View full text Add to dashboard Cite

A round-robin tournament is a contest where each and every player plays with all the other players. In this study, we propose a round-robin based tournament selection operator for the genetic algorithms (GAs). At first, we divide the whole population into two equal and disjoint groups, then each individual of a group competes with all the individuals of other group. Statistical experimental results reveal that the devised selection operator has a relatively better selection pressure along with a minimal loss of population diversity. For the consisting of assigned probability distribution with sampling algorithms, we employ the Pearson’s chi-square and the empirical distribution function as goodness of fit tests for the analysis of statistical properties analysis. At the cost of a nominal increase of the complexity as compared to conventional selection approaches, it has improved the sampling accuracy. Finally, for the global performance, we considered the traveling salesman problem to measure the efficiency of the newly developed selection scheme with respect to other competing selection operators and observed an improved performance.

show abstract

“…When used for parameter optimisation, the algorithm mostly explored solutions globally that need to be exploited to achieve convergence. This exploration process takes a long time and requires many iterations, resulting in lost opportunities to achieve convergence, and sometimes even being trapped in premature convergence (Goldanloo & Gharehchopogh, 2022;Naqvi & Shad, 2021;Rizal & Suyanto, 2020;Xi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction

Musa,

Ku-Mahamud,

Sardi Salim

et al. 2024

JICT

View full text Add to dashboard Cite

Energy consumption planning of an area is very important. It is essential to accurately predict the amount of short-term power required by an area using a highly effective prediction technique. The real-value-genetics-algorithm (RVGA) is the most effective technique that is currently used. However, the RVGA has some drawbacks, including the fact that it gets caught in premature convergence even when the search is performed over long iterations. This study proposes a hybrid prediction algorithm which comprises the RVGA and the extended-Nelder-Mead (ENM) algorithm. The ENM was implemented to speed up the search for the best among all solutions produced by the RVGA. The RVGA was configured to run under small iterations, and the ENM was used to achieve convergence. Experiments were performed on historical datasets containing the monthly electricity demand of the Gorontalo area, a region in Indonesia. The performance of the hybrid algorithm was compared to the hybrid Genetic Algorithm-Particle Swarm Optimisation (GA-PSO) and Real Coded-Genetic Algorithm (RC-GA) energy demand models based on the mean-absolute-percentage-error (MAPE), mean-square-error (MSE), root-mean-square-error (RMSE), and mean-absolute-deviation (MAD) error rates. The results showed that the proposed hybrid algorithm’s MAPE, MSE, RMSE, and MAD errors were 2.95 percent, 0.13 percent, 0.36 percent and 1.29 percent, respectively. Based on the accuracy measure obtained from this study, it implies that the RVGAENM hybrid is the best model for forecasting monthly electricity demand.

show abstract

Seeking a balance between population diversity and premature convergence for real-coded genetic algorithms with crossover operator

Cited by 11 publications

References 35 publications

Installation of Smog-Free Towers Using Novel Real Coded Genetic Algorithm

Installation of Smog-Free Towers Using Novel Real Coded Genetic Algorithm

Genetic algorithm with a new round-robin based tournament selection: Statistical properties analysis

Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction

Contact Info

Product

Resources

About