Knowledge-Intensive Evolutionary Algorithms for Solving a Healthcare Fleet Optimization Problem

2018 ◽  
pp. 192-223
Author(s):  
Carlos Adrian Catania ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Pierre Collet
Keyword(s):  
Evolutionary Algorithms ◽  
Knowledge Engineering ◽  
Domain Ontology ◽  
Real Problem ◽  
Fitness Functions ◽  
Evaluation Functions ◽  
Mutation Operators ◽  
Knowledge Intensive ◽  
Definition Of ◽  
Crossover And Mutation

In this chapter, the authors show how knowledge engineering techniques can be used to guide the definition of evolutionary algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. Various representations of the fitness functions, the genome, and mutation/crossover operators adapted to different types of problems (routing, scheduling, etc.) have been proposed in the literature. However, real problems including specific constraints (legal restrictions, specific usages, etc.) are often overlooked by the proposed generic models. To ensure that these constraints are effectively considered, the authors propose a methodology based on the structuring of the conceptual model underlying the problem, as a labelled domain ontology suitable for optimization by EA. The authors show that a precise definition of the knowledge model with a labelled domain ontology can be used to describe the chromosome, the evaluation functions, and the crossover and mutation operators. The authors show the details for a real implementation and some experimental results.

Ontologies to Lead Knowledge Intensive Evolutionary Algorithms

2016 ◽  
Vol 7 (1) ◽  
pp. 78-100 ◽  
Author(s):  
Carlos Adrian Catania ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Pierre Collet
Keyword(s):  
Evolutionary Algorithms ◽  
Knowledge Engineering ◽  
Domain Ontology ◽  
Real Problem ◽  
Genetic Operators ◽  
Fitness Functions ◽  
Generic Models ◽  
Intractable Problems ◽  
Knowledge Intensive ◽  
Definition Of

Evolutionary Algorithms (EA) have proven to be very effective in optimizing intractable problems in many areas. However, real problems including specific constraints are often overlooked by the proposed generic models. The authors' goal here is to show how knowledge engineering techniques can be used to guide the definition of Evolutionary Algorithms (EA) for problems involving a large amount of structured data, through the resolution of a real problem. They propose a methodology based on the structuring of the conceptual model underlying the problem, in the form of a labelled domain ontology suitable for optimization by EA. The case studyfocuses on the logistics involved in the transportation of patients. Although this problem belongs to the well-known family of Vehicle Routing Problems, its specificity comes from the data and constraints (cost, legal and health considerations) that must be taken into account. The precise definition of the knowledge model with thelabelled domain ontology permits the formal description of the chromosome, the fitness functions and the genetic operators.

scholarly journals Penerapan Algoritma Genetika Pada Penentuan Lintasan Terpendek Jalur Bus Rapid Transit Makassar

2019 ◽  
Vol 16 (2) ◽  
pp. 114
Author(s):  
Jusmawati Massalesse
Keyword(s):  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Departure Point ◽  
Fitness Functions ◽  
Bus Stop ◽  
Mutation Operators ◽  
Roulette Wheel ◽  
Bus Stops ◽  
Crossover And Mutation ◽  
Crossover Method

Bus Rapid Transit is a bus system that is fast, convenient, safe and on time from infrastructure, vehicles, and schedules. As a graph problem, BRT representation in a graph is done by assuming the bus stop as a vertex and the distance between bus stops is an edge. The problem examined in this paper is to find out the path that passes through all the bus stops with the smallest total distance, where the trip starts and ends at the same point, and all bus stops are crossed exactly once. The method used is the Genetic Algorithm, which works using objective and fitness functions, and combines selection, crossover and mutation operators to find the best solution. Using the roulette wheel, OX crossover method and a 0.07 of the probability of mutation, the distance of traverse from and to the departure point after passing all bus stops is 19.66 km or 12.22 miles.

Using FMEA models and ontologies to build diagnostic models

2001 ◽  
Vol 15 (4) ◽  
pp. 281-293 ◽  
Author(s):  
BURTON H. LEE
Keyword(s):  
Knowledge Sharing ◽  
Domain Knowledge ◽  
Knowledge Engineering ◽  
Knowledge Exchange ◽  
Domain Ontology ◽  
Theory And Practice ◽  
Product Model ◽  
Local Product ◽  
Common Domain ◽  
Knowledge Intensive

Product design and diagnosis are, today, worlds apart. Despite strong areas of overlap at the ontological level, traditional design process theory and practice does not recognize diagnosis as a part of the modeling process chain; neither do diagnosis knowledge engineering processes reference design modeling tasks as a source of knowledge acquisition. This paper presents the DAEDALUS knowledge engineering framework as a methodology for integrating design and diagnosis tasks, models, and modeling environments around a common Domain Ontology and Product Models Library. The approach organizes domain knowledge around the execution of a set of tasks in an enterprise product engineering task workflow. Each task employs a Task Application which uses a customized subset of the Domain Ontology—the Task Ontology—to construct a graphical Product Model. The Ontology is used to populate the models with relevant concepts (variables) and relations (relationships), thus serving as a concept dictionary-style mechanism for knowledge sharing and reuse across the different Task Applications. For inferencing, each task employs a local Problem-solving Method (PSM), and a Model-PSM Mapping, which operate on the local Product Model to produce reasoning outcomes. The use of a common Domain Ontology across tasks and models facilitates semantic consistency of variables and relations in constructing Bayesian networks for design and diagnosis.The approach is motivated by inefficiencies encountered in cleanly exchanging and integrating design FMEA and diagnosis models. Demonstration software under development is intended to illustrate how the DAEDALUS framework can be applied to knowledge sharing and exchange between Bayesian network-based design FMEA and diagnosis modeling tasks. Anticipated limitations of the DAEDALUS methodology are discussed, as is its relationship to Tomiyama's Knowledge Intensive Engineering Framework (KIEF). DAEDALUS is grounded in formal knowledge engineering principles and methodologies established during the past decade. Finally, the framework is presented as one possible approach for improved integration of generalized design and diagnostic modeling and knowledge exchange.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

TABU PROGRAMMING: A NEW PROBLEM SOLVER THROUGH ADAPTIVE MEMORY PROGRAMMING OVER TREE DATA STRUCTURES

2011 ◽  
Vol 10 (02) ◽  
pp. 373-406 ◽  
Author(s):  
ABDEL-RAHMAN HEDAR ◽  
EMAD MABROUK ◽  
MASAO FUKUSHIMA
Keyword(s):  
Search Algorithm ◽  
Benchmark Problems ◽  
Problem Solver ◽  
Learning Tools ◽  
Mutation Operators ◽  
Tree Data ◽  
Tree Data Structure ◽  
Comprehensive Framework ◽  
Rate Of Success ◽  
Crossover And Mutation

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favorably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.

Generative Optimization for Automatic Creation of 3D CAD Platforms

2021 ◽  
Author(s):  
William F. Quintero-Restrepo ◽  
Brian K. Smith ◽  
Junfeng Ma
Keyword(s):  
Evolutionary Algorithms ◽  
Six Sigma ◽  
Design Space ◽  
Optimization Method ◽  
Efficiency Improvement ◽  
3D Cad ◽  
Systems Model ◽  
Electric Machinery ◽  
Definition Of ◽  
Six Sigma Approach

Abstract The efficient creation of 3D CAD platforms can be achieved by the optimization of their design process. The research presented in this article showcases a method for allowing such efficiency improvement. The method is based on the DMADV six sigma approach. During the Define step, the definition of the scope and design space is established. In the Measure step, the initial evaluation of the platforms to be improved is done with the help of a Metrics framework for 3D CAD platforms. The Analyze Step includes the identification and optimization of the systems’ model of the process based on the architecture and the multiple objectives required for the improvement. The optimization method used that is based on evolutionary algorithms allows for the identification of the best improvement alternatives for the next step. During Design step of the method, the improvement alternatives are planned and executed. In the final Verification step, the evaluation of the improved process is tested against the previous status with the help of the Metrics Framework for 3D CAD platforms. The method is explained with an example case of a 3D CAD platform for creating metallic boxes for electric machinery.

Simulation of the Pressure-Bearing Capacity Test Model in Architectural Decoration Design

2014 ◽  
Vol 716-717 ◽  
pp. 391-394
Author(s):  
Li Mei Guo ◽  
Ai Min Xiao
Keyword(s):  
Genetic Algorithm ◽  
Bearing Capacity ◽  
Test Method ◽  
Test Model ◽  
Improved Genetic Algorithm ◽  
Good Convergence ◽  
Mutation Operators ◽  
Process Pressure ◽  
Calculation Process ◽  
Crossover And Mutation

in architectural decoration process, pressure-bearing capacity test is the foundation of design, and is very important. To this end, a pressure-bearing capacity test method in architectural decoration design is proposed based on improved genetic algorithm. The selection, crossover and mutation operators in genetic algorithm are improved respectively. Using its fast convergence characteristics eliminate the pressure movement in the calculation process. The abnormal area of pressure-bearing existed in buildings which can ensure to be tested is added, to obtain accurate distribution information of the abnormal area of pressure-bearing. Simulation results show that the improved genetic algorithm has good convergence, can accurately test the pressure-bearing capacity in architectural decoration.

scholarly journals RISK ECONOMY

2018 ◽  
pp. 16-55
Author(s):  
S. SOLODOVNICOV.
Keyword(s):  
High Tech ◽  
Negative Consequences ◽  
Theoretical Substantiation ◽  
Economic Concept ◽  
Stage Of Development ◽  
Global Catastrophe ◽  
Knowledge Intensive ◽  
Definition Of ◽  
Political Economic ◽  
Current Stage

The article is devoted to the theoretical substantiation of a new social paradigm – risk economy. The current stage of society development and the economy is characterized by a critical increase in financial, technological and technological, political and economic, geo-economic and other uncertainties. It is impossible to understand their ontological nature and reveal the phenomenological specificity without a meaningful definition of the current stage of development of the economic system of society. The article consistently revealed the characteristics of current society, which allowed the author to present a new political and economic concept that characterizes the current stage of development of society and the economy – the risk economy. The risk economy is an economy of high-tech and knowledge-intensive industries, characterized by the highest degree of political, economic, technological, financial and environmental uncertainties and risks. These risks are becoming comprehensive, many of them are in principle unpredictable, and their possible negative consequences could lead Humanity to a global catastrophe. Understanding the nature of risk economics is critically important for developing effective political and economic mechanisms to counter these risks.

scholarly journals Independence Conservation and Evolutionary Algorithms

2020 ◽  
Vol 2 (1) ◽  
pp. 32-35
Author(s):  
Eric Holloway
Keyword(s):  
Information Theory ◽  
Evolutionary Algorithms ◽  
Algorithmic Information Theory ◽  
Fitness Functions ◽  
Information Law ◽  
Algorithmic Information

Leonid Levin developed the first stochastic conservation of information law, describing it as "torturing an uninformed witness cannot give information about the crime."  Levin's law unifies both the deterministic and stochastic cases of conservation of information.  A proof of Levin's law from Algorithmic Information Theory is given as well as a discussion of its implications in evolutionary algorithms and fitness functions.

scholarly journals Forensic Engineering And The Scientific Method

2009 ◽  
Vol 26 (1) ◽  
Author(s):  
Laura L. Liptai
Keyword(s):  
Scientific Method ◽  
Knowledge Engineering ◽  
Analytical Framework ◽  
Education Experience ◽  
Forensic Engineering ◽  
Investigative Technique ◽  
Ultimate Objective ◽  
Definition Of ◽  
Prevention Methods ◽  
Engineering Investigation

The Scientific Method Is Utilized In Order To Understand The Relationship Among Observations Of Physical Phenomena, While Minimizing The Influence Of Human Bias And Maximizing Objectivity. Specific Procedures For The Application Of The Scientific Method Vary From One Field Of Science To Another, But The Investigative Technique Universally Provides For An Analytical Framework To Acquire, Collect And/Or Integrate Knowledge. Engineering Forensics Involves The Analysis Of The Parameters Or Cause(S) Of Incidents Or Failures And/Or Hypothetical Prevention Methods. Engineering Analysis Of Forensic Problems Is A Multifaceted, Multidisciplinary Pursuit That Is Often Wide In Scope. Forensic Engineering Generally Applies Existing Science In Conjunction With The Knowledge, Education, Experience, Training And Skill Of The Practitioner To Seek Solution(S). The Scientific Method, Including Definition Of A Null Hypothesis, Is Rarely Utilized In Forensics As New Science Is Rarely Required. A Forensic Engineering Investigation Typically Involves The Application Of Long Established Science (Newtons Laws, For Example). Forensic Engineering Encompasses The Systematic Search For Knowledge Necessitating The Observation And Definition Of A Problem; The Collection Of Data Through Observation, Research, Experimentation And/Or Calculation; The Analysis Of Data; And The Development And Evaluation Of Findings And Opinions. The Ultimate Objective Of A Forensic Engineering Investigation Is Uncompromised Data Collection And Systematically Considered, Iteratively Derived And Objectively Balanced Conclusions.

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