Applications of Genetic Algorithms to the Determination of Reaction Mechanisms

2006 ◽  
Author(s):  
John Ross ◽  
Igor Schreiber ◽  
Marcel O. Vlad
Keyword(s):  
Genetic Algorithms ◽  
Rate Coefficient ◽  
Optimization Problems ◽  
Chemical Species ◽  
Optimization Methods ◽  
Computational Method ◽  
Rate Coefficients ◽  
Next Generation ◽  
Binary Coding ◽  
Fitness Value

The mathematical computational method of genetic algorithms is frequently useful in solving optimization problems in systems with many parameters, for example, a search for suitable parameters of a given problem that achieves a stated purpose. The method searches for these parameters in an efficient parallel way, and has some analogies with evolution. There are other optimization methods available, such as stimulated annealing, but we shall use genetic algorithms. We shall present three different problems that give an indication of the diversity of applications. We begin with a very short primer on genetic algorithms, which can be omitted if the reader has some knowledge of this subject. Genetic algorithms (GAs) work with a coding of a parameter set, which in the field of chemical kinetics may consist of a number of parameters, such as rate coefficients; variables and constraints, such as concentrations; and other quantities such as chemical species. Binary coding for a parameter is done as follows. Suppose we have a rate coefficient = 9.08 × 10−7; then if we write that rate coefficient as 10−P , with −10 ≤ P ≤ 10, a binary coding with string length of 16 bits is given by . . . P = 10 − 20 R /(216 − 1) (10.1) . . . where 0 ≤ R ≤ 216 − 1. Since P = 6.04 we have R = 12,971, or R = 0011001010101010 to the base 2. Thus the value of the rate coefficient is encoded in a single bit string, called a chromosome. For the solution of a given problem an optimization criterion must be chosen. With a given choice of parameters this criterion is calculated; the comparison of that calculation with the goal set for the criterion gives a fitness value for that set of parameters. If the fitness is adequate but not sufficient, when both are selected by prior choice, for any individual, then retain that individual for the next generation. Reject individuals below that choice. Select individuals for the next generation with a probability proportional to the fitness value from a roulette wheel on which the slot size is proportional to the fitness value. Notice that genetic algorithms use probabilistic, not deterministic, transition rules.

Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 343-351 ◽  
Author(s):  
L. G. Caldas ◽  
L. K. Norford
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Hvac Systems ◽  
Design Problems ◽  
Building Walls ◽  
Building Form ◽  
And Control ◽  
Future Work

Many design problems related to buildings involve minimizing capital and operating costs while providing acceptable service. Genetic algorithms (GAs) are an optimization method that has been applied to these problems. GAs are easily configured, an advantage that often compensates for a sacrifice in performance relative to optimization methods selected specifically for a given problem, and have been shown to give solutions where other methods cannot. This paper reviews the basics of GAs, emphasizing multi-objective optimization problems. It then presents several applications, including determining the size and placement of windows and the composition of building walls, the generation of building form, and the design and operation of HVAC systems. Future work is identified, notably interfaces between a GA and both simulation and CAD programs.

scholarly journals Genetic Algorithms as Computational Methods for Finite-Dimensional Optimization

2021 ◽  
pp. 5-14
Author(s):  
Nataliya Gulayeva ◽  
Volodymyr Shylo ◽  
Mykola Glybovets
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Numerical Methods ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Direct Search ◽  
Mathematical Programming Problem ◽  
Combinatorial Optimization Problems

Introduction. As early as 1744, the great Leonhard Euler noted that nothing at all took place in the universe in which some rule of maximum or minimum did not appear [12]. Great many today’s scientific and engineering problems faced by humankind are of optimization nature. There exist many different methods developed to solve optimization problems, the number of these methods is estimated to be in the hundreds and continues to grow. A number of approaches to classify optimization methods based on various criteria (e.g. the type of optimization strategy or the type of solution obtained) are proposed, narrower classifications of methods solving specific types of optimization problems (e.g. combinatorial optimization problems or nonlinear programming problems) are also in use. Total number of known optimization method classes amounts to several hundreds. At the same time, methods falling into classes far from each other may often have many common properties and can be reduced to each other by rethinking certain characteristics. In view of the above, the pressing task of the modern science is to develop a general approach to classify optimization methods based on the disclosure of the involved search strategy basic principles, and to systematize existing optimization methods. The purpose is to show that genetic algorithms, usually classified as metaheuristic, population-based, simulation, etc., are inherently the stochastic numerical methods of direct search. Results. Alternative statements of optimization problem are given. An overview of existing classifications of optimization problems and basic methods to solve them is provided. The heart of optimization method classification into symbolic (analytical) and numerical ones is described. It is shown that a genetic algorithm scheme can be represented as a scheme of numerical method of direct search. A method to reduce a given optimization problem to a problem solvable by a genetic algorithm is described, and the class of problems that can be solved by genetic algorithms is outlined. Conclusions. Taking into account the existence of a great number of methods solving optimization problems and approaches to classify them it is necessary to work out a unified approach for optimization method classification and systematization. Reducing the class of genetic algorithms to numerical methods of direct search is the first step in this direction. Keywords: mathematical programming problem, unconstrained optimization problem, constrained optimization problem, multimodal optimization problem, numerical methods, genetic algorithms, metaheuristic algorithms.

scholarly journals Representasi Matriks untuk Proses Crossover Pada Algoritma Genetika untuk Optimasi Travelling Salesman Problem

Matematika ◽  
2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Ismi Fadhillah ◽  
Yurika Permanasari ◽  
Erwin Harahap
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Travelling Salesman Problem ◽  
Optimal Number ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Fitness Value ◽  
The City

Abstrak. Travelling Salesman Problem (TSP) merupakan salah satu permasalahan optimasi kombinatorial yang biasa terjadi dalam kehidupan sehari-hari. Permasalahan TSP yaitu mengenai seseorang yang harus mengunjungi semua kota tepat satu kali dan kembali ke kota awal dengan jarak tempuh minimal. TSP dapat diselesaikan dengan menggunakan metode Algoritma Genetika. Dalam Algoritma Genetika, representasi matriks merupakan representasi kromosom yang menunjukan sebuah perjalanan. Jika dalam perjalanan tersebut melewati n kota maka akan dibentuk matriks n x n. Matriks elemen Mij dengan baris i dan kolom j dimana entry M(i,j) akan bernilai 1 jika dan hanya jika kota i dikunjungi sebelum kota j dalam satu perjalanan tersebut, selain itu M(i,j)=0. Crossover adalah mekanisme yang dimiliki algoritma genetika dengan menggabungkan dua kromosom sehingga menghasilkan anak kromosom yang mewarisi ciri-ciri dasar dari parent. Algoritma Genetika selain melibatkan populasi awal dalam proses optimasi juga membangkitkan populasi baru melalui proses crossover, sehingga dapat memberikan daftar variabel yang optimal bukan hanya solusi tunggal. Dari hasil proses crossover dalam contoh kasus TSP melewati 6 kota, terdapat 2 kromosom anak terbaik dengan nilai finess yang sama yaitu 0.014. Algoritma Genetika dapat berhenti pada generasi II karena berturut-turut mendapat nilai fitness tertinggi yang tidak berubahKata kunci : Travelling Salesman Program (TSP), Algoritma Genetika, Representasi Matriks, Proses Crossover Abstract. Travelling Salesman Problem (TSP) is one of combinatorial optimization problems in everyday life. TSP is about someone who had to visit all the cities exactly once and return to the initial city with minimal distances. TSP can be solved using Genetic Algorithms. In a Genetic Algorithm, a matrix representation represents chromosomes which indicates a journey. If in the course of the past n number of city will set up a matrix n x n. The matrix element Mij with row i and column j where entry M (i, j) will be equal to 1 if and only if the city i before the city j visited in one trip. In addition to the M (i, j) = 0. Crossover is a mechanism that is owned by the Genetic Algorithm to combine the two chromosomes to produce offspring inherited basic characteristics of the parent. Genetic Algorithms in addition to involve the population early in the optimization process will also generate new populations through the crossover process, so as to provide optimal number of variables is not just a single solution. From the results of the crossover process in the case of TSP passing through six cities, there are two the best offspring with the same finess value which is 0.014. Genetic Algorithms can be stopped on the second generation due to successive received the highest fitness value unchanged.Keywords: Travelling Salesman Program (TSP), Genetic Algorithm, Matrix Representation, Crossover Process

Reaction of •OH with CHCl=CH-CHF2 and its atmospheric implication for future environmental-friendly refrigerant

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Olivier Holtomo ◽  
Lydia Rhyman ◽  
Mama Nsangou ◽  
Ponnadurai Ramasami ◽  
Ousmanou Motapon
Keyword(s):  
Gas Phase ◽  
Cross Sections ◽  
Radiative Forcing ◽  
Rate Coefficient ◽  
Computational Method ◽  
Rate Coefficients ◽  
Ir Absorption ◽  
Geometrical Structures ◽  
Atmospheric Lifetime ◽  
Environmental Friendly

Abstract In order to understand the atmospheric implication of the chlorinated hydrofluoroolefin (HFO), the geometrical structures and the IR absorption cross sections of the stereoisomers 1-chloro-3,3-difluoropropene were studied using the B3LYP/6-31G(3df) and M06-2X/6-31G(3df) methods in the gas phase. The cis-trans isomerization was assessed using the M06-2X/6-311++G(3df,p)//6-31+G(3df,p) method. The latter method was also employed for thermochemistry and the rate coefficients of the reactions of •OH with the cis- and trans-isomers in the temperature ranging from 200 to 400 K. The computational method CCSD/cc-pVTZ//M06-2X/6-31+G(3df,p) was used to benchmark the rate coefficients. It turns out that, the trans-isomer is more stable than cis-isomer and the trans- to cis-isomerization is thermodynamically unfavorable. The rate coefficient follows the Gaussian law with respect to the inverse of temperature. At the global temperature of stratosphere, the calculated rate coefficients served to estimate the atmospheric lifetime along with the photochemical ozone creation potential (POCP). This yielded lifetimes of 4.31 and 7.31 days and POCPs of 3.80 and 2.23 for the cis- and trans-isomer, respectively. The radiative forcing efficiencies gave 0.0082 and 0.0152 W m−2 ppb−1 for the cis- and trans-isomer, respectively. The global warming potential approached zero for both stereoisomers at 20, 100, and 500 years time horizons.

scholarly journals Redundant binary codes in genetic algorithms: multi-objective design optimization of water distribution networks

2020 ◽  
Author(s):  
Tiku T. Tanyimboh
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Water Distribution ◽  
Optimization Problems ◽  
Search Space ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Binary Codes ◽  
Multi Objective ◽  
Binary Coding

Abstract Genetic algorithms have been shown to be highly effective for optimization problems in various disciplines, and binary coding is generally adopted as it is straightforward to implement and lends itself to problems with discrete-valued decision variables. However, a difficulty associated with binary coding is the existence of redundant codes that do not correspond to any element in the finite discrete set that the encoded parameter belongs to. A common technique used to address redundant binary codes is to discard the chromosomes in which they occur. Effective alternatives to the outright removal of redundant codes are lacking in the literature. This article presents illustrative examples based on the problem of optimizing the design of water distribution networks. Two benchmark networks in the literature and two different multi-objective design optimization models were considered. Different fixed mapping schemes gave significantly different solutions in the search space. The main inference from the results is that mapping schemes that improved diversity in the population of solutions achieved better results, which may pave the way for the development of practical and effective mapping schemes.

scholarly journals Application of Genetic Algorithm in Common Optimization Problems

2019 ◽  
Vol 8 (1) ◽  
pp. 17-21
Author(s):  
Nika Topuria ◽  
Omar Kikvidze
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Local Optima ◽  
Deterministic Algorithms ◽  
Complex Optimization ◽  
Short Time ◽  
Fitness Parameter

Use of non-deterministic algorithms for solving multi-variable optimization problems is widely used nowadays. Genetic Algorithm belongs to a group of stochastic biomimicry algorithms, it allows us to achieve optimal or near-optimal results in large optimization problems in exceptionally short time (compared to standard optimization methods). Major advantage of Genetic Algorithm is the ability to fuse genes, to mutate and do selection based on fitness parameter. These methods protect us from being trapped in local optima (Most of deterministic algorithms are prone to getting stuck on local optima). In this paper we experimentally show the upper hand of Genetic Algorithms compared to other traditional optimization methods by solving complex optimization problem.

scholarly journals GenHap: A novel computational method based on genetic algorithms for haplotype assembly

2017 ◽  
Author(s):  
Andrea Tangherloni ◽  
Simone Spolaor ◽  
Leonardo Rundo ◽  
Marco S Nobile ◽  
Ivan Merelli ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Selection Process ◽  
Fitness Function ◽  
Computational Method ◽  
Nucleotide Polymorphisms ◽  
Optimization Strategy ◽  
Genetic Operators ◽  
Chromosome Conformation ◽  
Haplotype Assembly ◽  
Fitness Value

The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process. Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.

Metaheuristics in Data Mining

2011 ◽  
pp. 1200-1206
Author(s):  
Miguel García Torres
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
High Performance ◽  
Optimization Problems ◽  
Scatter Search ◽  
Optimization Methods ◽  
Population Based ◽  
Variable Neighbourhood Search ◽  
Neighbourhood Search ◽  
Distribution Algorithms

The Metaheuristics are general strategies for designing heuristic procedures with high performance. The term metaheuristic, which appeared in 1986 for the first time (Glover, 1986), is compound by the terms: “meta”, that means over or behind, and “heuristic”. Heuristic is the qualifying used for methods of solving optimization problems that are obtained from the intuition, expertise or general knowledge (Michalewicz & Fogel, 2000). Nowadays a lot of known strategies can be classified as metaheuristics and there are a clear increasing number of research papers and applications that use this kind of methods. Several optimization methods that already existed when the term appeared have been later interpreted as metaheuristics (Glover & Kochenberger, 2003). Genetic Algorithms, Neural Networks, Local Searches, and Simulated Annealing are some of those classical metaheuristics. Several modern metaheuristics have succeeded in solving relevant optimization problems in industry, business and engineering. The most relevant among them are Tabu Search, Variable Neighbourhood Search and GRASP. New population based evolutionary metaheuristics such as Scatter Search and Estimation Distribution Algorithms are also quite important. Besides Neural Networks and Genetic Algorithms, other nature-inspired metaheuristics such as Ant Colony Optimization and Particle Swarm Optimization are also now well known metaheuristics.

scholarly journals GenHap: A novel computational method based on genetic algorithms for haplotype assembly

2017 ◽  
Author(s):  
Andrea Tangherloni ◽  
Simone Spolaor ◽  
Leonardo Rundo ◽  
Marco S Nobile ◽  
Ivan Merelli ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Selection Process ◽  
Fitness Function ◽  
Computational Method ◽  
Nucleotide Polymorphisms ◽  
Optimization Strategy ◽  
Genetic Operators ◽  
Chromosome Conformation ◽  
Haplotype Assembly ◽  
Fitness Value

The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process. Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.

scholarly journals Distributed parameter tuning for genetic algorithms

2010 ◽  
Vol 7 (3) ◽  
pp. 661-677
Author(s):  
David Barrero ◽  
Antonio González-Pardo ◽  
David Camacho ◽  
María R-Moreno
Keyword(s):  
Genetic Algorithms ◽  
Regular Expression ◽  
Optimization Problems ◽  
Biological Evolution ◽  
Parameter Tuning ◽  
Distributed Parameter ◽  
Large Set ◽  
Agent Based ◽  
Fitness Value ◽  
Fitting In

Genetic Algorithms (GA) is a family of search algorithms based on the mechanics of natural selection and biological evolution. They are able to efficiently exploit historical information in the evolution process to look for optimal solutions or approximate them for a given problem, achieving excellent performance in optimization problems that involve a large set of dependent variables. Despite the excellent results of GAs, their use may generate new problems. One of them is how to provide a good fitting in the usually large number of parameters that must be tuned to allow a good performance. This paper describes a new platform that is able to extract the Regular Expression that matches a set of examples, using a supervised learning and agent-based framework. In order to do that, GA-based agents decompose the GA execution in a distributed sequence of operations performed by them. The platform has been applied to Language induction problem, for that reason the experiments are focused on the extraction of the regular expression that matches a set of examples. Finally, the paper shows the efficiency of the proposed platform (in terms of fitness value) applied to three case studies: emails, phone numbers and URLs. Moreover, it is described how the codification of the alphabet affects to the performance of the platform.

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