New Factorial Design Theoretic Crossover Operator for Parametrical Problem

Crossover is the main genetic operator which influences the power of evolutionary algorithms. Among a variety of crossover operators, there has been a growing interest in multi-parent crossover operators in evolutionary computation. The main motivation of those schemes is establishing comprehensive collective collaboration of more than two chromosomes in the population to generate a new offspring. In this paper, a novel all-parent crossover operator called collective crossover for genetic algorithm is proposed. In this method, all individuals in the current population are involved in recombination part and one offspring is generated. The contribution of each individuals is defined based on its quality in terms of fitness value. The performance of the collective crossover operator is tested on CEC2017 benchmark functions. The results revealed that the proposed crossover operator performs better when compared to well-known two-parent crossover operators including one-point and two-point crossovers. In addition, the differences between collective crossover and the other crossover operators are statistically significant for the most cases.

Chapter Ten - Differential evolution

The effects of two new crossover operators on genetic algorithm performance

Crossover operators with multiple descendents produce more than two offspring for each pair of parents. They were suggested as an alternative method to the common practice of generating only two offspring per couple. An offspring selection mechanism is responsible for choosing the two offspring that become the children contributed by the mating. Recently, there has been an increasing interest in incorporating this crossover scheme into real-coded genetic algorithm models because its operation was particularly suitable to attain reliable and accurate solutions for many continuous optimization problems. In this paper, we undertake an extensive empirical study of the main factors that affect the performance of real-parameter crossover operator with multiple descendents. To do this, we focus our attention on three well-known neighborhood-based real-parameter crossover operators, BLX-α, fuzzy recombination, and PNX. The experimental results obtained confirm that the generation of multiple descendents along with the offspring selection mechanism that chooses the two best offspring may enhance the operation of these three crossover operators. Another important finding from our experiments is that real-coded genetic algorithms with crossover operators with multiple descendents are more efficient than standard real-coded genetic algorithms, that is, they offer solutions with higher quality, requiring fewer fitness function evaluations. © 2008 Wiley Periodicals, Inc.

HTB underground gas storage (UGS) is a gas storage facility rebuilt from a depleted gas reservoir. It is located in Changji Xinjiang Province, in northwestern China. With designed working gas volume 45×108m3 and peak working rate 27×106m3 per day, HTB UGS is the biggest UGS constructed in China. Since commissioning in June 2013, HTB UGS has been injecting gas 113.8×108m3, and withdrawing gas 78.2×108m3 until now. In order to analyze its production performance after 7 years operations, we figured out a systematic analysis method. First of all, we built a production database for HTB UGS, collecting daily injection-production data, tubing pressure, and kinds of testing data of the storage. Also, we built a database for all 30 injection-production wells recording their geologic information, drilling information, gas injection-production information, etc. Based on detailed data, we analyzed the operating characteristics of the HTB UGS from 6 aspects: injection-production gas volume, liquid production volume, operating pressure, reservoir connectivity laterally and vertically, sealing of the trap and well-patten spacing. It shows that 1) gas injection and production volume of the storage increases year by year, accounting for 20% working gas volume of UGS in China. 2) Oil and water production are relatively low. The gas is becoming more and more like dry gas. 3) The operating pressure during gas injection almost reaches maximum allowable storage pressure (34 MPa); while during gas withdrawal it reaches 26 MPa, far above the minimum storage pressure (18 MPa). 4) Overall reservoir connectivity is good in the lateral direction and it is disconnected in the vertical direction between E1-2z21 and E1-2z22. 5) The storage is well-sealed and there is no gas leakage. 6) The well-patten covers 80% of the gas storage. To sum up, HTB UGS works well generally from 2013 to 2020, playing an increasingly significant role in summer and winter seasons in China. However, in order to guarantee gas withdrawal in limited time (120 days in winter time in China), well pattern infilling is still needed in layer E1-2z22. The construction of UGS in China started in 1999. At present, 27 gas storage facilities have been built and it is planned to build another 23 to generate more than 400×108m3 working gas volume by the end of 2030. However, a systematic performance analysis method for UGS has not been applied. This paper will offer the reference for production performance analysis of UGS.

In many industries including metallurgy, petrochemicals, paper, aerospace, pharmaceutical, electronics, ceramic and automotive manufacturing, the production system can contain a machine bottleneck which processes, in some cases, all the jobs. The management of this machine is crucial for the enterprise because it is responsible for delays in customers' orders delivery. The jobs scheduling on this machine is a way of approaching the problem and treats the whole production system. The single machine model provides a basis for more complicated environment such those seen in many real cases. Also, several studies have shown that several jobs, if not all, have time-dependent setup times. So, decision makers must therefore organize the jobs scheduling on this single machine by trying to minimize downtime while respecting the different deadlines. On the other hand, several resolution methods have been developed in literature to solve this kind of problem using approaching methods like metaheuristics or hybrid methods integrating exact methods. This thesis is in this direction. In this research, we propose several effective approaches to solve the single machine scheduling problem with sequence-dependent setup times with the objective to minimize total tardiness of the jobs. First, a new crossover operator in a genetic algorithm is proposed. This algorithm shows its ability to be competitive with the best approaches found in the literature on benchmark problems. We thus highlight the importance of incorporating specific problem knowledge into genetic operators, even if classical genetic operators could be used. However, the results obtained show that the proposed genetic algorithm is less efficient in comparison to the best resolution method found in the literature. Indeed, we noticed that the proposed algorithm lacks an intensification process for enhancing its. To overcome this shortcoming, we explore resolution class methods that showed promising prospects in the last decade. Even, the hybrid algorithms have achieved very good results in a wide variety of problems. In recent years, several researchers have introduced the hybridization of metaheuristics with exact methods. In fact, this kind of hybridization can be an attractive alternative because both methods have very different characteristics that can be combined to produce better results. Thus we explore the design of hybrid genetic algorithms improving the intensification process of the proposed genetic algorithm by integrating an exact method and mechanisms from other resolution methods to improve the performance. However, we find few approaches hybridizing metaheuristics and constraint programming in the literature to solve scheduling problems. In a second step, we propose a modeling for the total tardiness single machine with setup-dependent times scheduling problem using the constraint-based scheduling and the ILOG C++ API. The related experiments show that algorithm efficiency improves with specific tree search strategies and heuristic scheduling variables. Under prohibitive time computing, the proposed approach results are generally far from the best known solutions. However, we got interesting performance on small instances to consider allowing its hybridization with other resolution methods. In a third step, we introduce a hybrid approach based on the integration between genetic algorithm and constraint based scheduling approaches. This latter approach is integrated in the reproduction and the intensification processes of a genetic algorithm autonomously. Finally, we propose a hybrid algorithm which integrates several concepts from constraint programming, multi-objective optimization and ant colonies optimization in a crossover operator into a genetic algorithm. The proposed hybrid algorithms show excellent performance on benchmark problems from the literature by improving many of the best known solutions for some of them.

Genetic algorithms (GA) are mostly used for feature selection in literature. In this study, multi-parent crossover operators are used in genetic algorithms for feature selection. The uniform crossover, occurrence-based crossover, fitness-based crossover, and diagonal crossover are considered as multi-parent crossover operators. Binary representation is used for encoding a candidate solution in GA. The empirical evaluation of these operators is performed on three different datasets with different numbers of features. We perform Oneway ANOVA and Tukey HSD tests at a confidence level of 95 % for statistical comparisons of algorithms. The experiments are conducted into two parts: (1) In this part, we investigate the effect of the number of parents for each multi-parent crossover operator, (2) we compare the performance of these crossover operators with the best setting. The results reveal that occurrence-based crossover with 5 parents outperforms the other crossover operators, however it selects more attributes.

Design of a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations.

The travelling salesman problem (TSP) is a benchmark problem in which a salesman has to visit all nodes (cities) in a network exactly once except the starting node, come back to the starting node and find the shortest tour. Genetic algorithm (GA) is one of the best algorithms to deal with the travelling salesman problem (TSP). In GA, crossover operator plays a vital role and the sequential constructive crossover (SCX) is one of the best crossover operators for solving the TSP. Several improvements have been proposed for other crossover operators. In this paper we propose four improved genetic algorithms using three local search methods – 2-opt search, a hybrid mutation, and a combined mutation operator, and incorporate them into SCX. The experimental results on some TSPLIB instances show that our improved GAs can significantly improve simple GA using SCX in terms of solution quality.

UDC 69:65.016 ; JEL Classification: D23, L74The purpose: to determine the overall impact of the factors relevant to the construction industry and the impact of additional internal factors of the country's construction industry subjects during the analyzed period. Research Methodology: assessment is possible using an effective, thorough and reliable analysis methodology. Proposed assessing methodology of the effectiveness of strategies development of Ukrainian construction industry enterprises takes into account a comprehensive and wide range of valuation indicators (sales revenue of construction products, volumes of construction products production, market share in accordance with the planned and actual parameters of the development strategy implementation). The following methods were used: - methods of system analysis and synthesis to consider theoretical aspects of the effectiveness of development strategies; - methods of statistical analysis for studying the financial condition of Ukrainian construction enterprises; - methods of economic and mathematical analysis to determine quantitative assessment and evaluation indicators; - formal logic methods for conclusions development and ways to improving the effectiveness of development strategies of Ukrainian construction enterprises. Results: the calculation and evaluation of the effectiveness of the planned (declared) development strategy implementation of the five construction industry enterprises of Ukraine for 2015-2017 were conducted. The estimation procedure of the most significant influence of factors on the level of implementation effectiveness of strategies of the country's construction industry enterprises was given. The presented results of the state and transformations of the implementation effectiveness level of the planned (declared) development strategies allows to form the basic provisions of the formation historiography of this sphere for the specified period. these include: lack of investment in development, despite the need to upgrade the fixed assets and technologies used to implement strategic parameters; insufficient implementation of innovations of construction methods management, waste management, management of energy efficiency of construction products and construction (it is established that only two (SC JSC “BK “Ukrbud” and PJSC “HK “Kyivmiskbud”) are used the innovations and only one enterprise uses effective innovations implementation (SC JSC “BK “Ukrbud”). The most significant influence of factors on the effectiveness level of the strategies implementation of the surveyed enterprises during 2015–2017 was estimated. Scientific novelty: for the first time the determination of the overall impact of the general factors of action relevant to the construction industry and the impact of additional internal factors of the subjects of the countrie's construction industry during the analyzed period is substantiated. It is established that, unlike the existing approach, an assessment of the impact of a set of these factors of development of Ukrainian construction enterprises, which are investigated through their positive or negative effect on the state of effectiveness of the implementation of planned (declared) development strategies. It should be noted that all values of these factors for the individual specified categories were calculated within the framework of this study. It is proved that in order to determine the most significant influence of factors on the effectiveness level of strategies implementation of the studied country's construction enterprises, and their integrated assessment was carried out. Practical value: the presented study allows to estimate the level of internal resistance to the influence of external, general internal (sectoral) and international factors by the largest enterprises of the construction industry of Ukraine during 2015-2017. The investigated results of the factors influence state is a historiography of the strategic development of the subjects of the industry during the specified period, which can be used to compare by parameters of development the counteracting of factor influence for other enterprises in this industry (medium, small, etc.).

Floorplanning is a crucial process in the early stage of VLSI physical design. It determines the performance, reliability, and size of chips. B*-tree is a simple yet efficient representation that encodes the layout of modules in a compact and non-slicing structure. Several B*-tree variants and corresponding operators have been proposed to deal with non-slicing floorplanning. However, these operators are considered and applied individually. A collective manipulation of them remains missing. This study proposes a genetic algorithm (GA) that enables multiple crossover and mutation operators for solving the non-slicing floorplanning problem. In particular, the GA selects one crossover operator and one mutation operator from the pool of operators whenever reproducing an offspring. The probability for an operator to be selected is based on its empirical performance. This study conducts experiments on two well-known benchmarks to examine the effectiveness of the proposed method. The experimental results show that the GA can achieve superior solution quality and efficiency on the non-slicing VLSI floorplanning.

Introduction. Differences in the understanding of the main management and information and security approaches to the formation of an innovative cluster group of SME with large industrial enterprises indicate the ambiguity of ways to measure and evaluate such interaction and its scope, which in Covid-19 on the basis of sustainability, decentralization and European integration requires further study. Methods. The historical and logical method is used in the article; the method of systematization, classification and theoretical generalization; the method of institutional analysis; the method of system analysis; the method of logical analysis and synthesis; the method of graphical analysis. Results. It is proved that the cooperation of industrial SME with large industrial enterprises makes certain demands on them. Before attempting to cooperate with large and medium-sized industrial enterprises, manufacturing SME must have a clear idea of the state of their activities, their own potential and its use, the market position of goods and services relative to competitors. Therefore, the procedure of diagnostics of the state of activity of industrial SME is developed in the work, the leading one in which is the assessment of its solvency. The competitiveness of a production SME, its potential and sustainability are assessed using available methods. Production SME differ in scale and types of activity, policy of its financing, degree of integration into the economy of the region or production cluster, which determines the multiplicity of areas of their support. Discussion. Market support for manufacturing SME should be directed to intensify their activities - assistance in finding markets, customers and business partners, the use of mechanisms for adaptation of enterprises using the full range of opportunities for administrative support. Cooperation of manufacturing SME with large industrial enterprises as a kind of business partnership can take place not only in traditional forms, but also using its most common organizational and economic models - subcontracting, franchising, leasing and venture business. The use of such a range of organizational and economic models of cooperation will help meet the interests of each of its participants and strengthen the economy of the region. Keywords: modern management, information and security approaches, innovative cluster group, COVID-19, sustainability, decentralization, European integration.

Development of a Mutation Operator in a Real-Coded Genetic Algorithm for Bridge Model Optimization

Genetic algorithm crossover operators for ordering applications

Intelligent Machine Evolutionary Algorithm Learning Based on Artificial Intelligence