Selection of an Optimum Feed Profile Using genetic Algorithms

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows. Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds. Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance. Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds. Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method.

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Multiple Crossover per Couple with Selection of the Two Best Offspring: An Experimental study with the BLX-α Crossover Operator for Real-Coded Genetic Algorithms

Advances in Artificial Intelligence — IBERAMIA 2002 - Lecture Notes in Computer Science ◽

10.1007/3-540-36131-6_40 ◽

2002 ◽

pp. 392-401 ◽

Cited By ~ 8

Author(s):

F. Herrera ◽

M. Lozano ◽

E. Pérez ◽

A.M. Sánchez ◽

P. Villar

Keyword(s):

Experimental Study ◽

Genetic Algorithms ◽

Crossover Operator ◽

Real Coded Genetic Algorithms ◽

Selection Of

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Optimalisasi Penyelesaian Knapsack Problem Dengan Algoritma Genetika

Lontar Komputer Jurnal Ilmiah Teknologi Informasi ◽

10.24843/lkjiti.2016.v07.i03.p06 ◽

2016 ◽

pp. 182

Author(s):

I Wayan Supriana

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Everyday Life ◽

Knapsack Problem ◽

Selection Process ◽

Optimization Algorithms ◽

Evolutionary Process ◽

Knapsack Problems ◽

Theory Of Evolution ◽

Selection Of

Knapsack problems is a problem that often we encounter in everyday life. Knapsack problem itself is a problem where a person faced with the problems of optimization on the selection of objects that can be inserted into the container which has limited space or capacity. Problems knapsack problem can be solved by various optimization algorithms, one of which uses a genetic algorithm. Genetic algorithms in solving problems mimicking the theory of evolution of living creatures. The components of the genetic algorithm is composed of a population consisting of a collection of individuals who are candidates for the solution of problems knapsack. The process of evolution goes dimulasi of the selection process, crossovers and mutations in each individual in order to obtain a new population. The evolutionary process will be repeated until it meets the criteria o f an optimum of the resulting solution. The problems highlighted in this research is how to resolve the problem by applying a genetic algorithm knapsack. The results obtained by the testing of the system is built, that the knapsack problem can optimize the placement of goods in containers or capacity available. Optimizing the knapsack problem can be maximized with the appropriate input parameters.

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The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations

Evolutionary Computation ◽

10.1162/evco.1999.7.3.231 ◽

1999 ◽

Vol 7 (3) ◽

pp. 231-253 ◽

Cited By ~ 166

Author(s):

George Harik ◽

Erick Cantú-Paz ◽

David E. Goldberg ◽

Brad L. Miller

Keyword(s):

Genetic Algorithms ◽

Building Blocks ◽

Correct Selection ◽

Problem Size ◽

One Dimensional ◽

Fitness Evaluation ◽

Gambler’S Ruin ◽

Gambler's Ruin Problem ◽

Selection Of

This paper presents a model to predict the convergence quality of genetic algorithms based on the size of the population. The model is based on an analogy between selection in GAs and one-dimensional random walks. Using the solution to a classic random walk problem—the gambler's ruin—the model naturally incorporates previous knowledge about the initial supply of building blocks (BBs) and correct selection of the best BB over its competitors. The result is an equation that relates the size of the population with the desired quality of the solution, as well as the problem size and difficulty. The accuracy of the model is verified with experiments using additively decomposable functions of varying difficulty. The paper demonstrates how to adjust the model to account for noise present in the fitness evaluation and for different tournament sizes.

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Feature selection of hyperspectral data by considering the integration of Genetic Algorithms and Particle Swarm Optimization

10.1117/12.2065472 ◽

2014 ◽

Cited By ~ 1

Author(s):

Pedram Ghamisi ◽

Jon Atli Benediktsson

Keyword(s):

Genetic Algorithms ◽

Feature Selection ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Hyperspectral Data ◽

Swarm Optimization ◽

Selection Of

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Breeding Theorem Proving Heuristics with Genetic Algorithms

10.29007/gms9 ◽

2018 ◽

Author(s):

Simon Schäfer ◽

Stephan Schulz

Keyword(s):

Genetic Algorithms ◽

Large Scale ◽

First Order ◽

Proof Search ◽

Theorem Provers ◽

Search Heuristics ◽

Practical Performance ◽

Set Up ◽

The Given ◽

Selection Of

First-order theorem provers have to search for proofs in an infinitespace of possible derivations. Proof search heuristics play a vitalrole for the practical performance of these systems. In the currentgeneration of saturation-based theorem provers like SPASS, E,Vampire or Prover~9, one of the most important decisions is theselection of the next clause to process with the given clausealgorithms. Provers offer a wide variety of basic clause evaluationfunctions, which can often be parameterized and combined in manydifferent ways. Finding good strategies is usually left to the usersor developers, often backed by large-scale experimentalevaluations. We describe a way to automatize this process usinggenetic algorithms, evaluating a population of different strategieson a test set, and applying mutation and crossover operators to goodstrategies to create the next generation. We describe the design andexperimental set-up, and report on first promising results.

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