scholarly journals Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Qiang Lu ◽  
Jun Ren ◽  
Zhiguang Wang
Keyword(s):  
Experimental Data ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Search Space ◽  
Research Field ◽  
Benchmark Problems ◽  
Regression Problem ◽  
Mathematical Expressions ◽  
The Right

A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, sinceGPhas to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge andGP(PFK-GP) is proposed to reduce the space ofGPsearching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population,PFK-GPfinds the right formulas quickly by exploring the search space of data features. We have comparedPFK-GPwith ParetoGPon regression of eight benchmark problems. The experimental results confirm that thePFK-GPcan reduce the search space and obtain the significant improvement in the quality of SR.

An exploration-enhanced elephant herding optimization

2019 ◽  
Vol 36 (9) ◽  
pp. 3029-3046 ◽  
Author(s):  
Islam A. ElShaarawy ◽  
Essam H. Houssein ◽  
Fatma Helmy Ismail ◽  
Aboul Ella Hassanien
Keyword(s):  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Search Space ◽  
Exploration And Exploitation ◽  
Content Type ◽  
Discrepancy Measure ◽  
The One ◽  
Star Discrepancy ◽  
Exploration Phase

Purpose The purpose of this paper is to propose an enhanced elephant herding optimization (EEHO) algorithm by improving the exploration phase to overcome the fast-unjustified convergence toward the origin of the native EHO. The exploration and exploitation of the proposed EEHO are achieved by updating both clan and separation operators. Design/methodology/approach The original EHO shows fast unjustified convergence toward the origin specifically, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms. Furthermore, the star discrepancy measure is adopted to quantify the quality of the exploration phase of evolutionary algorithms in general. Findings In experiments, EEHO has shown a better performance of convergence rate compared with the original EHO. Reasons behind this performance are: EEHO proposes a more exploitative search method than the one used in EHO and the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Operator γ is added to EEHO assists to escape from local optima, which commonly exist in the search space. The proposed EEHO controls the convergence rate and the random walk independently. Eventually, the quantitative and qualitative results revealed that the proposed EEHO outperforms the original EHO. Research limitations/implications Therefore, the pros and cons are reported as follows: pros of EEHO compared to EHO – 1) unbiased exploration of the whole search space thanks to the proposed update operator that fixed the unjustified convergence of the EHO toward the origin and the proposed separating operator that fixed the tendency of EHO to introduce new elephants at the boundary of the search space; and 2) the ability to control exploration–exploitation trade-off by independently controverting the convergence rate and the random walk using different parameters – cons EEHO compared to EHO: 1) suitable values for three parameters (rather than two only) have to be found to use EEHO. Originality/value As the original EHO shows fast unjustified convergence toward the origin specifically, the search method adopted in EEHO is more exploitative than the one used in EHO because of the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Further, the star discrepancy measure is adopted to quantify the quality of exploration phase of evolutionary algorithms in general. Operator γ that added EEHO allows the successive local and global searching (exploration and exploitation) and helps escaping from local minima that commonly exist in the search space.

scholarly journals A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

2019 ◽  
Vol 27 (3) ◽  
pp. 467-496 ◽  
Author(s):  
Su Nguyen ◽  
Yi Mei ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Systems ◽  
Search Space ◽  
Automated Design ◽  
Dispatching Rules ◽  
Programming Algorithm ◽  
Local Search Heuristic

Designing effective dispatching rules for production systems is a difficult and time-consuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

On the Performance of Different Genetic Programming Approaches for the SORTING Problem

2015 ◽  
Vol 23 (4) ◽  
pp. 583-609 ◽  
Author(s):  
Markus Wagner ◽  
Frank Neumann ◽  
Tommaso Urli
Keyword(s):  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Additional Benefit ◽  
Variable Length ◽  
Optimization Process ◽  
Sorting Problem ◽  
The Cost ◽  
Multiobjective Algorithms ◽  
Theoretical Results

In genetic programming, the size of a solution is typically not specified in advance, and solutions of larger size may have a larger benefit. The flexibility often comes at the cost of the so-called bloat problem: individuals grow without providing additional benefit to the quality of solutions, and the additional elements can block the optimization process. Consequently, problems that are relatively easy to optimize cannot be handled by variable-length evolutionary algorithms. In this article, we analyze different single- and multiobjective algorithms on the sorting problem, a problem that typically lacks independent and additive fitness structures. We complement the theoretical results with comprehensive experiments to indicate the tightness of existing bounds, and to indicate bounds where theoretical results are missing.

Organic Evolution and Problem Solving

1996 ◽  
Author(s):  
Thomas Bäck
Keyword(s):  
Artificial Intelligence ◽  
Evolutionary Algorithms ◽  
Current Knowledge ◽  
Random Search ◽  
Optimization Technique ◽  
Search Space ◽  
Research Field ◽  
Organic Evolution ◽  
Mathematical Terminology ◽  
The Moment

Evolutionary Algorithms (EAs), the topic of this work, is an interdisciplinary research field with a relationship to biology, Artificial Intelligence, numerical optimization, and decision support in almost any engineering discipline. Therefore, an attempt to cover at least some of these relations must necessarily result in several introductory pages, always having in mind that it hardly can be complete. This is the reason for a rather voluminous introduction to the fundamentals of Evolutionary Algorithms in section 1.1 without giving any practically useful description of the algorithms now. At the moment, it is sufficient to know that these algorithms are based on models of organic evolution, i.e., nature is the source of inspiration. They model the collective learning process within a population of individuals, each of which represents not only a search point in the space of potential solutions to a given problem, but also may be a temporal container of current knowledge about the “laws” of the environment. The starting population is initialized by an algorithm-dependent method, and evolves towards successively better regions of the search space by means of (more or less) randomized processes of recombination, mutation, and selection. The environment delivers a quality information (fitness value) for new search points, and the selection process favors those individuals of higher quality to reproduce more often than worse individuals. The recombination mechanism allows for mixing of parental information while passing it to their descendants, and mutation introduces innovation into the population. This process is currently used by three different mainstreams of Evolutionary Algorithms, i.e. Evolution Strategies (ESs), Genetic Algorithms (GAs), and Evolutionary Programming (EP), details of which are presented in chapter 2. This chapter presents their biological background in order to have the necessary understanding of the basic natural processes (section 1.1). Evolutionary Algorithms are then discussed with respect to their impact on Artificial Intelligence and, at the same time, their interpretation as a technique for machine learning (section 1.2). Furthermore, their interpretation as a global optimization technique and the basic mathematical terminology as well as some convergence results on random search algorithms as far as they are useful for Evolutionary Algorithms are presented in section 1.3.

A New Dominance Method Based on Expanding Dominated Area for Many-Objective Optimization

2019 ◽  
Vol 33 (03) ◽  
pp. 1959008
Author(s):  
Junhua Liu ◽  
Yuping Wang ◽  
Xingyin Wang ◽  
Si Guo ◽  
Xin Sui
Keyword(s):  
Evolutionary Algorithms ◽  
Selection Pressure ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Results ◽  
Benchmark Problems ◽  
Optimal Solutions ◽  
Pareto Dominance ◽  
Global Optimal

The performance of the traditional Pareto-based evolutionary algorithms sharply reduces for many-objective optimization problems, one of the main reasons is that Pareto dominance could not provide sufficient selection pressure to make progress in a given population. To increase the selection pressure toward the global optimal solutions and better maintain the quality of selected solutions, in this paper, a new dominance method based on expanding dominated area is proposed. This dominance method skillfully combines the advantages of two existing popular dominance methods to further expand the dominated area and better maintain the quality of selected solutions. Besides, through dynamically adjusting its parameter with the iteration, our proposed dominance method can timely adjust the selection pressure in the process of evolution. To demonstrate the quality of selected solutions by our proposed dominance method, the experiments on a number of well-known benchmark problems with 5–25 objectives are conducted and compared with that of the four state-of-the-art dominance methods based on expanding dominated area. Experimental results show that the new dominance method not only enhances the selection pressure but also better maintains the quality of selected solutions.

ANALYSIS OF REGULATIONS REGARDING THE SETTLEMENT OF ISSUES ON THE REMOVAL AND RECOVERY OF PLANTATIONS IN THE RIGHT-OF-WAY OF PUBLIC ROADS

2020 ◽  
pp. 55-60
Author(s):  
Nataliia Kharytonova ◽  
Olha Mykolaienko ◽  
Tetyana Lozova
Keyword(s):  
Traffic Safety ◽  
Climatic Factors ◽  
Visual Orientation ◽  
Right Of Way ◽  
Adverse Weather ◽  
Overhead Power Lines ◽  
The Right ◽  
Neck Diameter ◽  
Proper Operation

Greening of roads contributes to the protection of roads and their elements from influence of adverse weather and climatic factors; it includes the measures for improvement and landscaping of roads, ensures the protection of roadside areas from transport pollution, provides visual orientation of drivers. The solution of these issues will ensure creation and maintenance of safe and comfortable conditions for travelers. Green plantings in the right-of-way road area include woody, bushy, flower and grass vegetation of natural and artificial origin. For proper operation of public roads and satisfaction of other needs of the industry, there may be the need in removing the greenery. The reason for the removal of greenery in the right-of-way road area may be due to the following factors: construction of the architectural object, widening of the motor road, repair works in the security zone of overhead power lines, water supply, drainage, heating, telecommunications facilities, cutting of hazardous, dry and fautal trees, as well as self-grown and brushwood trees with a root neck diameter not exceeding 5 cm, elimination of the consequences of natural disasters and emergencies. The removal of plantations in the right-of-way area is executed in order to ensure traffic safety conditions and to improve the quality of plantations composition and their protective properties. Nowadays, in Ukraine there is no clear procedure for issuing permits for removing of such plantations. In order to resolve this issue, there is a need in determining the list of regulations in the area of forest resources of Ukraine and, if needed, the list of regulatory acts that have to be improved; to prepare a draft of the regulatory legal act that would establish the procedure of plantations cutting, the methodology of their condition determination, recovery costs determination, the features of cutting. Keywords: plantations, cutting, right-of-way, woodcutting permit, order.

Орошение томата F1 Донской в открытом грунте Московской области

2019 ◽  
Author(s):  
D.I. Engalychev ◽  
N.A. Engalycheva ◽  
A.M. Menshikh
Keyword(s):  
Experimental Data ◽  
Agricultural Technology ◽  
Tomato Fruits ◽  
River Floodplain ◽  
Yield And Quality ◽  
Open Ground ◽  
Alluvial Meadow ◽  
Standard Production ◽  
Moscow River

Представлены экспериментальные данные о влиянии капельного орошения на урожайность и качество плодов томата при выращивании культуры в открытом грунте Московской области. На плодородных аллювиальных луговых почвах Москворецкой поймы при соблюдении агротехники без орошения в среднем за три года исследований в полевых условиях получена урожайность томата F1 Донской 31,9 т/га, с орошением 48,5 т/га, в т.ч. стандартной продукции 42,6 т/га.The article presents experimental data on the effect of drip irrigation on the yield and quality of tomato fruits when growing crops in open ground of the Moscow Region. On fertile alluvial meadow soils of the Moscow river floodplain, with the observance of agricultural technology without irrigation, the field yield of tomato hybrid F1 Donskoi on average for three years of research was 31.9 t/ha, with irrigation 48.5 t/ha, incl. standard production 42.6 t/ha.

Modifikasi Model Pembelajaran Problem Based Learning (PBL) dengan Strategi Pembelajaran Tugas dan Paksa

2019 ◽  
Vol 2 ◽  
Author(s):  
Tita Mila Mustofani ◽  
Ita Hartinah
Keyword(s):  
Critical Thinking ◽  
Learning Strategies ◽  
Student Motivation ◽  
Problem Based Learning ◽  
Learning Model ◽  
Learning Material ◽  
Task Strategies ◽  
The Right ◽  
Increasing Student Learning

This writing aims to help teachers to increase motivation, activity, creativity, and critical thinking of students in solving problems in class. The way to increase student motivation in learning in class is to choose the right learning model with ongoing learning material. One learning model that increases students' creativity and critical thinking in problem solving is a Problem Based Learning (PBL) learning model. To improve students' insights in order to easily solve problems there is a need to do tasks, if students do not do the task then they must accept the agreed upon consequences when making learning contracts, thus modifying the Problem Based Learning (PBL) learning model with task strategies and forced. The results of the modification of learning with the Problem Based Learning (PBL) learning model through forced and forced strategies are expected to improve the learning process so that students become more disciplined and do not waste time doing assignments. The advantages of modifying the Problem Based Learning (PBL) learning model with task and forced learning strategies are increasing student learning motivation, improving the quality of learning, training students' understanding by giving assignments continuously, teaching discipline to students in order to be accountable for tasks assigned, and reducing laziness in students.

THE VALUE OF LOVE IN THE SYSTEM OF FORMATION OF THE PROFESSIONAL MATURITY OF THE EDUCATOR

2020 ◽  
Vol 1 (10(79)) ◽  
pp. 12-18
Author(s):  
G. Bubyreva
Keyword(s):  
Russian Federation ◽  
The State ◽  
Educational Process ◽  
Wide Range ◽  
The Family ◽  
The Russian Federation ◽  
The Government ◽  
The Individual ◽  
The Right

The existing legislation determines the education as "an integral and focused process of teaching and upbringing, which represents a socially important value and shall be implemented so as to meet the interests of the individual, the family, the society and the state". However, even in this part, the meaning of the notion ‘socially significant benefit is not specified and allows for a wide range of interpretation [2]. Yet the more inconcrete is the answer to the question – "who and how should determine the interests of the individual, the family and even the state?" The national doctrine of education in the Russian Federation, which determined the goals of teaching and upbringing, the ways to attain them by means of the state policy regulating the field of education, the target achievements of the development of the educational system for the period up to 2025, approved by the Decree of the Government of the Russian Federation of October 4, 2000 #751, was abrogated by the Decree of the Government of the Russian Federation of March 29, 2014 #245 [7]. The new doctrine has not been developed so far. The RAE Academician A.B. Khutorsky believes that the absence of the national doctrine of education presents a threat to national security and a violation of the right of citizens to quality education. Accordingly, the teacher has to solve the problem of achieving the harmony of interests of the individual, the family, the society and the government on their own, which, however, judging by the officially published results, is the task that exceeds the abilities of the participants of the educational process.  The particular concern about the results of the patriotic upbringing served as a basis for the legislative initiative of the RF President V. V. Putin, who introduced the project of an amendment to the Law of RF "About Education of the Russian Federation" to the State Duma in 2020, regarding the quality of patriotic upbringing [3]. Patriotism, considered by the President of RF V. V. Putin as the only possible idea to unite the nation is "THE FEELING OF LOVE OF THE MOTHERLAND" and the readiness for every sacrifice and heroic deed for the sake of the interests of your Motherland. However, the practicing educators experience shortfalls in efficient methodologies of patriotic upbringing, which should let them bring up citizens, loving their Motherland more than themselves. The article is dedicated to solution to this problem based on the Value-sense paradigm of upbringing educational dynasty of the Kurbatovs [15].

Evaluation of staff in Healthcare Companies

2020 ◽  
Author(s):  
Troncone Raffaella ◽  
Coda Marco
Keyword(s):  
Social Context ◽  
Evaluation System ◽  
Daily Life ◽  
Good Health ◽  
Rehabilitation Services ◽  
Individual Contribution ◽  
Personnel Evaluation ◽  
Good Evaluation ◽  
The Right

Evaluation is at the basis of any social context where all individuals are simultaneously "evaluated" and "evaluators" in all areas of daily life. The goal of a good evaluation system is to encourage staff to do "Good Health" through the provision of quality prevention, diagnosis, treatment and rehabilitation services. The main reasons that lead to the evaluation of the personnel lie in the inevitable and primary importance of the human resource in achieving the corporate objectives, and by the pressing need for the quality of the service provided to the citizen, as well as the legitimate need of the employee to differentiate, clarifying its specificities and its own individual contribution to the general objectives of the company. In the working context, the "personnel evaluation" assumes a fundamental importance, if managed with the right criteria, in order to make the employee not a simple pawn to move and manage for use and consumption of the organization, but an integral part of the organization itself.

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