No more trial and error for zeolites

Watcharop Chaikittisilp; Tatsuya Okubo

doi:10.1126/science.abm0089

Enhancing genetic algorithms using multi mutations

10.7287/peerj.preprints.2187 ◽

2016 ◽

Author(s):

Ahmad B Hassanat ◽

Esra’a Alkafaween ◽

Nedal A Alnawaiseh ◽

Mohammad A Abbadi ◽

Mouhammd Alkasassbeh ◽

...

Keyword(s):

Genetic Algorithms ◽

Novel Mutation ◽

Mutation Operator ◽

Trial And Error ◽

Travelling Salesman ◽

Selection Strategies ◽

Mutation Operators ◽

Global Optima ◽

More Trial ◽

Selection Of

Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error. This paper investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. Novel mutation operators are proposed, in addition to two selection strategies for the mutation operators, one of which is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) were conducted to evaluate the proposed methods, and these were compared to the well-known exchange mutation and rearrangement mutation. The results show the importance of some of the proposed methods, in addition to the significant enhancement of the genetic algorithm’s performance, particularly when using more than one mutation operator.

Download Full-text

Analogical Thinking for Generation of Innovative Ideas: An Exploratory Study of Influential Factors

Interdisciplinary Journal of Information Knowledge and Management ◽

10.28945/3539 ◽

2016 ◽

Vol 11 ◽

pp. 201-214 ◽

Cited By ~ 1

Author(s):

Eunyoung Kim ◽

Hideyuki Horii

Keyword(s):

Exploratory Study ◽

Influential Factors ◽

Trial And Error ◽

Analogical Thinking ◽

New Ideas ◽

Thinking Process ◽

Definition Of ◽

The Relationship ◽

More Trial ◽

New Strategies

Analogical thinking is one of the most effective tools to generate innovative ideas. It enables us to develop new ideas by transferring information from well-known domains and utilizing them in a novel domain. However, using analogical thinking does not always yield appropriate ideas, and there is a lack of consensus among researchers regarding the evaluation methods for assessing new ideas. Here, we define the appropriateness of generated ideas as having high structural and low superficial similarities with their source ideas. This study investigates the relationship between thinking process and the appropriateness of ideas generated through analogical thinking. We conducted four workshops with 22 students in order to collect the data. All generated ideas were assessed based on the definition of appropriateness in this study. The results show that participants who deliberate more before reaching the creative leap stage and those who are engaged in more trial and error for deciding the final domain of a new idea have a greater possibility of generating appropriate ideas. The findings suggest new strategies of designing workshops to enhance the appropriateness of new ideas.

Download Full-text

PROFILE OF STUDENT'S MATHEMATICAL PROBLEM SOLVING ABILITY BASED ON LINGUISTIC INTELLIGENCE AND MATHEMATICAL LOGICAL INTELLIGENCE

Prima Jurnal Pendidikan Matematika ◽

10.31000/prima.v5i1.3224 ◽

2021 ◽

Vol 5 (1) ◽

pp. 64

Author(s):

Hestu Wilujeng ◽

Edi Irawan ◽

Prahesti Tirta Safitri ◽

Aan Subhan Pamungkas

Keyword(s):

Mathematical Logic ◽

Problem Solving ◽

Qualitative Study ◽

Mathematical Problem ◽

Mathematical Problem Solving ◽

Trial And Error ◽

The Subject ◽

Basic Characteristics ◽

More Trial

The process of solving problems carried out by students in stages, namely understanding problems, planning solutions, carrying out solutions and checking again. Solving student problems varies according to the basic characteristics of students' interests, talents and potential. Learning will be more optimal if it is adjusted to the intelligence possessed by students. The goal is that teachers can facilitate learning according to the intelligence possessed by students, so the teacher must know the intelligence possessed by students. This research is a qualitative study using two subjects, namely the subject of linguistics and the subject of mathematical logic. The results showed that at the problem-understanding stage, SLM completed using formulas, completed according to plan and checked by recalculating. SL uses more trial-and-error reasoning, understanding information by reading sentences quickly as well as checking again.

Download Full-text

Enhancing genetic algorithms using multi mutations

10.7287/peerj.preprints.2187v1 ◽

2016 ◽

Cited By ~ 2

Author(s):

Ahmad B Hassanat ◽

Esra’a Alkafaween ◽

Nedal A Alnawaiseh ◽

Mohammad A Abbadi ◽

Mouhammd Alkasassbeh ◽

...

Keyword(s):

Genetic Algorithms ◽

Novel Mutation ◽

Mutation Operator ◽

Trial And Error ◽

Travelling Salesman ◽

Selection Strategies ◽

Mutation Operators ◽

Global Optima ◽

More Trial ◽

Selection Of

Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error. This paper investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. Novel mutation operators are proposed, in addition to two selection strategies for the mutation operators, one of which is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) were conducted to evaluate the proposed methods, and these were compared to the well-known exchange mutation and rearrangement mutation. The results show the importance of some of the proposed methods, in addition to the significant enhancement of the genetic algorithm’s performance, particularly when using more than one mutation operator.

Download Full-text