Estimation of a simple genetic algorithm applied to a laboratory experiment

The aim of our contribution relies on studying the possibility of implementing a genetic algorithm in order to reproduce some characteristics of a simple laboratory experiment with human subjects. The novelty of our paper regards the estimation of the key-parameters of the algorithm, and the analysis of the characteristics of the estimator.Estimation, genetic algoritms, experimenst

Efficient genetic algorithms for solving hard constrained optimization problems

This paper studies many Genetic Algorithm strategies to solve hard-constrained optimization problems. It investigates the role of various genetic operators to avoid premature convergence. In particular, an analysis of niching methods is carried out on a simple function to show advantages and drawbacks of each of them. Comparisons are also performed on an original benchmark based on an electrode shape optimization technique coupled with a charge simulation metho

Analysis of Different Types of Regret in Continuous Noisy Optimization

The performance measure of an algorithm is a crucial part of its analysis. The performance can be determined by the study on the convergence rate of the algorithm in question. It is necessary to study some (hopefully convergent) sequence that will measure how "good" is the approximated optimum compared to the real optimum. The concept of Regret is widely used in the bandit literature for assessing the performance of an algorithm. The same concept is also used in the framework of optimization algorithms, sometimes under other names or without a specific name. And the numerical evaluation of convergence rate of noisy algorithms often involves approximations of regrets. We discuss here two types of approximations of Simple Regret used in practice for the evaluation of algorithms for noisy optimization. We use specific algorithms of different nature and the noisy sphere function to show the following results. The approximation of Simple Regret, termed here Approximate Simple Regret, used in some optimization testbeds, fails to estimate the Simple Regret convergence rate. We also discuss a recent new approximation of Simple Regret, that we term Robust Simple Regret, and show its advantages and disadvantages.Comment: Genetic and Evolutionary Computation Conference 2016, Jul 2016, Denver, United States. 201

The (1+(λ,λ))(1+(\lambda,\lambda)) Genetic Algorithm for Permutations

The $(1+(\lambda,\lambda))$ genetic algorithm is a bright example of an evolutionary algorithm which was developed based on the insights from theoretical findings. This algorithm uses crossover, and it was shown to asymptotically outperform all mutation-based evolutionary algorithms even on simple problems like OneMax. Subsequently it was studied on a number of other problems, but all of these were pseudo-Boolean. We aim at improving this situation by proposing an adaptation of the $(1+(\lambda,\lambda))$ genetic algorithm to permutation-based problems. Such an adaptation is required, because permutations are noticeably different from bit strings in some key aspects, such as the number of possible mutations and their mutual dependence. We also present the first runtime analysis of this algorithm on a permutation-based problem called Ham whose properties resemble those of OneMax. On this problem, where the simple mutation-based algorithms have the running time of $\Theta(n^2 \log n)$ for problem size $n$, the $(1+(\lambda,\lambda))$ genetic algorithm finds the optimum in $O(n^2)$ fitness queries. We augment this analysis with experiments, which show that this algorithm is also fast in practice.Comment: This contribution is a slightly extended version of the paper accepted to the GECCO 2020 workshop on permutation-based problem

VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.

The Genetic Algorithm is an area in the field of Artificial Intelligence that is founded on the principles of biological evolution. Visualization techniques help in understanding the searching behaviour of Genetic Algorithm. lt also makes possible the user interactions during the searching process. It is noted that active user intervention increases the acceleration of Genetic Algorithm towards an optimal solution. In proposed research work, the user is aided by a visualization based on the representation of multidimensional Genetic Algorithm data on 2-0 space. The aim of the proposed approach is to study the benefit of using visualization techniques to explorer Genetic Algorithm data based on gene values. The user participates in the search by proposing a new individual. This is difTerent from existing Interactive Genetic Algorithm in which selection and evaluation of solutions is done by the users. A tool termed as VIGA-20 (Visualization of Genetic Algorithm using 2-0 Graph) is implemented to accomplish this goal. This visual tool enables the display of the evolution of gene values from generation to generation to observing and analysing the behaviour of the search space with user interactions. Individuals for the next generation are selected by using the objective function. Hence, a novel humanmachine interaction is developed in the proposed approach. The efficiency of the proposed approach is evaluated by two benchmark functions. The analysis and comparison of VIGA-20 is based on convergence test against the results obtained from the Simple Genetic Algorithm. This comparison is based on the same parameters except for the interactions of the user. The application of proposed approach is the modelling the branching structures by deriving a rule from best solution of VIGA-20. The comparison of results is based on the different user's perceptions, their involvement in the VIGA-20 and the difference of the fitness convergence as compared to Simple Genetic Algorithm

Structural reliability analysis using response surface method with improved genetic algorithm

For the conventional computational methods for structural reliability analysis, the common limitations are long computational time, large number of iteration and low accuracy. Thus, a new novel method for structural reliability analysis has been proposed in this paper based on response surface method incorporated with an improved genetic algorithm. The genetic algorithm is first improved from the conventional genetic algorithm. Then, it is used to produce the response surface and the structural reliability is finally computed using the proposed method. The proposed method can be used to compute structural reliability easily whether the limit state function is explicit or implicit. It has been verified by two practical engineering cases that the algorithm is simple, robust, high accuracy and fast computation

Multiple damage detection and localization in beam-like and complex structures using co-ordinate modal assurance criterion combined with firefly and genetic algorithms

Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm

Comparative Analysis of Similarity Check Mechanism for Motif Extraction

In this work, a comparative analysis of the similarity check mechanism used in the most effective algorithm for mining simple motifs GEMS (Gene Enrichment Motif Searching) and that used in a popular multi-objective genetic algorithm, MOGAMOD (Multi-Objective Genetic Algorithm for Motif Discovery) was done. In our previous work, we had reported the implementation of GEMS on suffix tree –Suffix Tree Gene Enrichment Motif Searching (STGEMS) and shown the linear asymptotic runtime achieved. Here, we attempt to empirically proof the high sensitivity of the resulting algorithm, STGEMS in mining motifs from challenging sequences like we have in Plasmodium falciparum. The results obtained validates the high sensitivity of the similarity check mechanism employed in GEMS and also shows that a careful deployment of this mechanism in the multi-objective genetic algorithm, improved the sensiti