An Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions

2017 ◽  
pp. 65-86
Author(s):  
Keith Mathias ◽  
Darrell Whitley ◽  
Anthony Kusuma ◽  
Christof Stork
Keyword(s):  
Genetic Algorithms ◽  
Empirical Evaluation ◽  
Objective Functions

Applications of the Non-Dominated Sorting Genetic Algorithm (NSGA) in Chemical Reaction Engineering

2003 ◽  
Vol 1 (1) ◽  
Author(s):  
Anjana D Nandasana ◽  
Ajay Kumar Ray ◽  
Santosh K. Gupta
Keyword(s):  
Genetic Algorithms ◽  
Chemical Reaction ◽  
Optimization Problems ◽  
Research Direction ◽  
Reaction Engineering ◽  
Future Research ◽  
Engineering Optimization ◽  
Objective Functions ◽  
Chemical Reactors ◽  
Chemical Reaction Engineering

Most of the chemical reaction engineering optimization problems encounters more than one objective functions. A considerable amount of research has been reported on the multiobjective optimization of various chemical reactors using various non-dominated sorting genetic algorithms. This is reviewed in this paper. The introduction of the topic is given at the beginning, followed by the description of multi-objective optimization and Pareto set. We have then discussed various non-dominated sorting genetic algorithms and its applications in chemical reaction engineering. Some comments are also made on the future research direction in this area.

scholarly journals Information Theoretic Objective Function for Genetic Software Clustering

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 18
Author(s):  
Habib Izadkhah ◽  
Mahjoubeh Tajgardan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Objective Function ◽  
High Performance ◽  
Evolutionary Process ◽  
Program Comprehension ◽  
Software Systems ◽  
Objective Functions ◽  
Information Theoretic ◽  
Software Clustering

Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions’ limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

scholarly journals A self-reconfiguring architecture supporting multiple objective functions in genetic algorithms

2009 ◽  
Author(s):  
Charalampos Effraimidis ◽  
Kyprianos Papadimitriou ◽  
Apostolos Dollas ◽  
Ioannis Papaefstathiou
Keyword(s):  
Genetic Algorithms ◽  
Multiple Objective ◽  
Objective Functions

scholarly journals Multiple Changepoint Detection via Genetic Algorithms

2012 ◽  
Vol 25 (2) ◽  
pp. 674-686 ◽  
Author(s):  
Shanghong Li ◽  
Robert Lund
Keyword(s):  
Time Series ◽  
Genetic Algorithms ◽  
North Atlantic ◽  
Minimum Description Length ◽  
Correlated Data ◽  
Reference Station ◽  
Objective Functions ◽  
The North ◽  
Climate Time Series ◽  
Climatic Time Series

Abstract This paper studies genetic algorithms as a means of estimating the number of changepoints and their locations in a climatic time series. Such methods bypass classic subsegmentation algorithms, which sometimes yield suboptimal conclusions. Minimum description length techniques are introduced. These techniques require optimizing an objective function over all possible changepoint numbers and location times. The general objective functions allow for correlated data, reference station aspects, and/or nonnormal marginal distributions, all common features of climate time series. As an exhaustive evaluation of all changepoint configurations is not possible, the optimization is accomplished via a genetic algorithm that randomly walks through a subset of good models in an intelligent manner. The methods are applied in the analysis of 173 yr of annual precipitation measurements from New Bedford, Massachusetts, and the North Atlantic basin tropical cyclone record.

Application of Genetic Algorithms to the Synthesis of Riser Configurations

2003 ◽  
Author(s):  
Luciano T. Vieira ◽  
Beatriz de S. L. P. de Lima ◽  
Alexandre G. Evsukoff ◽  
Breno P. Jacob
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Random Choice ◽  
Large Space ◽  
Steel Catenary Riser ◽  
Deep Waters ◽  
Optimization Methodology

The purpose of this work is to describe the application of Genetic Algorithms in the search of the best configuration of catenary riser systems in deep waters. Particularly, an optimization methodology based on genetic algorithms is implemented on a computer program, in order to seek an optimum geometric configuration for a steel catenary riser in a lazy-wave configuration. This problem is characterized by a very large space of possible solutions; the use of traditional methods is an exhaustive work, since there is a large number of variables and parameters that define this type of system. Genetic algorithms are more robust than the more commonly used optimization techniques. They use random choice as a tool to guide a search toward regions of the search space with likely improvements. Some differences such as the coding of the parameter set, the search from a population of points, the use of objective functions and randomized operators are factors that contribute to the robustness of a genetic algorithm and result in advantages over traditional techniques. The implemented methodology has as baseline one or more criteria established by the experience of the offshore engineer. The implementation of an intelligent methodology oriented specifically to the optimization and synthesis of riser configurations will not only facilitate the work of manipulating a huge mass of data, but also assure the best alternative between all the possible ones, searching in a much larger space of possible solutions than classical methods.

scholarly journals Earthquake Design of Reinforced Concrete Buildings Using NSGA-II

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Herian A. Leyva ◽  
Edén Bojórquez ◽  
Juan Bojórquez ◽  
Alfredo Reyes-Salazar ◽  
José H. Castorena ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Reinforced Concrete ◽  
Seismic Design ◽  
Engineering Practice ◽  
Objective Functions ◽  
Rc Buildings ◽  
Nsga Ii ◽  
Earthquake Design ◽  
The Cost ◽  
Nondominated Sorting

In the present study, the optimal seismic design of reinforced concrete (RC) buildings is obtained. For this purpose, genetic algorithms (GAs) are used through the technique NSGA-II (Nondominated Sorting Genetic Algorithm), thus a multiobjective procedure with two objective functions is established. The first objective function is the control of maximum interstory drift which is the most common parameter used in seismic design codes, while the second is to minimize the cost of the structure. For this aim, several RC buildings are designed in accordance with the Mexico City Building Code (MCBC). It is assumed that the structures are constituted by rectangular and square concrete sections for the beams, columns, and slabs which are represented by a binary codification. In conclusion, this study provides complete designed RC buildings which also can be used directly in the structural and civil engineering practice by means of genetic algorithms. Moreover, genetic algorithms are able to find the most adequate structures in terms of seismic performance and economy.

Profiled Endwall Design Using Genetic Algorithms With Different Objective Functions

2011 ◽  
Author(s):  
Jamie McIntosh ◽  
Richard MacPherson ◽  
Grant Ingram ◽  
Simon Hogg
Keyword(s):  
High Pressure ◽  
Genetic Algorithms ◽  
Design Process ◽  
Turbine Blades ◽  
Design System ◽  
Steam Turbines ◽  
Objective Functions ◽  
Design Decisions ◽  
Gas Turbine Blades ◽  
Trade Offs

Profiled endwalls are a widely researched technology for reducing the secondary loss in turbines. Most designs in the literature have been produced directly by manufacturers and although general performance information is given the detailed design decisions are kept confidential. This paper outlines a simple design system for profiled endwalls that uses genetic algorithms to find an acceptable design. As the design process is produced in an academic environment full details of the design process, geometries produced, objective functions and the various trade-offs involved in the design are available and discussed in the paper. Two designs were produced using the design system: one using secondary kinetic energy as the objective function of the design system and the second using a U-cubed integral. The different designs that are produced with the different objective functions are discussed in detail in the paper. Finally profiled endwalls have traditionally been used in the high pressure stages of gas turbine blades, the paper also discusses the merits and challenges in applying these technologies to the high pressure and intermediate pressure stages of steam turbines.

scholarly journals Using Shapley Values and Genetic Algorithms to Solve Multiobjective Optimization Problems

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2021
Author(s):  
Hsien-Chung Wu
Keyword(s):  
Genetic Algorithms ◽  
Multiobjective Optimization ◽  
Cooperative Game ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Payoff Function ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Multiobjective Optimization Problems ◽  
Shapley Values

This paper proposes a new methodology to solve multiobjective optimization problems by invoking genetic algorithms and the concept of the Shapley values of cooperative games. It is well known that the Pareto-optimal solutions of multiobjective optimization problems can be obtained by solving the corresponding weighting problems that are formulated by assigning some suitable weights to the objective functions. In this paper, we formulated a cooperative game from the original multiobjective optimization problem by regarding the objective functions as the corresponding players. The payoff function of this formulated cooperative game involves the symmetric concept, which means that the payoff function only depends on the number of players in a coalition and is independent of the role of players in this coalition. In this case, we can reasonably set up the weights as the corresponding Shapley values of this formulated cooperative game. Under these settings, we can obtain the so-called Shapley–Pareto-optimal solution. In order to choose the best Shapley–Pareto-optimal solution, we used genetic algorithms by setting a reasonable fitness function.

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