Implementation of Feature Selection to Reduce the Number of Features in Determining the Initial Centroid of K-Means Algorithm

Clustering is a data mining method to group data based on its features or attributes. One reasonably popular clustering algorithm is K-Means. K-Means algorithm is often optimized with methods such as the genetic algorithm (GA) to overcome the problem of determining the initial random centroid. Many features in a dataset can reduce the accuracy and increase the computational time of model execution. Feature selection is an algorithm that can reduce data dimension by removing less relevant features for modeling. Therefore, this research will implement Feature selection on the K-Means algorithm optimized with the Dynamic Artificial Chromosome Genetic Algorithm (DAC GA). From the experimental results with ten datasets, it is found that reducing the number of features with feature selection can speed up the computation time of DAC GA to K-Means process by 17,5%. However, all experiments resulted in higher Sum of Square Distance (SSD) and Davies Bouldin Index (DBI) values in clustering results with selected features

Searching the solution space in constructive geometric constraint solving with genetic algorithms

Geometric problems defined by constraints have an exponential number of solution instances in the number of geometric elements involved. Generally, the user is only interested in one instance such that besides fulfilling the geometric constraints, exhibits some additional properties. Selecting a solution instance amounts to selecting a given root every time the geometric constraint solver needs to compute the zeros of a multi valuated function. The problem of selecting a given root is known as the Root Identification Problem. In this paper we present a new technique to solve the root identification problem. The technique is based on an automatic search in the space of solutions performed by a genetic algorithm. The user specifies the solution of interest by defining a set of additional constraints on the geometric elements which drive the search of the genetic algorithm. The method is extended with a sequential niche technique to compute multiple solutions. A number of case studies illustrate the performance of the method.Postprint (published version

What is the cost of maximizing ESG performance in the portfolio selection strategy? The case of The Dow Jones Index average stocks

[EN] Portfolio selection is one of the main financial topics. The original portfolio selection problem dealt with the trade-off between return and risk, measured as the mean returns and the variance, respectively. For investors more variables other than return and risk are considered to select the stocks to be included in the portfolio. Nowadays, many investors include corporate social responsibility as one eligibility criterion. Additionally, other return and risk measures are being employed. All of this, together with further constraints such as portfolio cardinality, which mirror real-world demands by investors, have made the multicriteria portfolio selection problem to be NP-hard. To solve this problem, heuristics such as the non-dominated sorting genetic algorithm II have been developed. The aim of this paper is to analyse the trade-off between return, risk and corporate social responsibility. To this end, we construct pareto efficient portfolios using a fuzzy multicriteria portfolio selection model with real-world constraints. The model is applied on a set of 28 stocks which are constituents of the Dow Jones Industrial Average stock index. The analysis shows that portfolios scoring higher in corporate social responsibility obtain lower returns. As of the risk, the riskier portfolios are those with extreme (high or low) corporate social responsibility scores. Finally, applying the proposed portfolio selection methodology, it is possible to build investment portfolios that dominate the benchmark. That is, socially responsible portfolios, measured by ESG scores, must not necessarily be penalized in terms of return or risk.García García, F.; Gankova-Ivanova, T.; González-Bueno, J.; Oliver-Muncharaz, J.; Tamosiuniene, R. (2022). What is the cost of maximizing ESG performance in the portfolio selection strategy? The case of The Dow Jones Index average stocks. Enterpreneurship and Sustainability Issues. 9(4):178-192. https://doi.org/10.9770/jesi.2022.9.3(9)1781929

A quantum genetic algorithm with quantum crossover and mutation operations

In the context of evolutionary quantum computing in the literal meaning, a quantum crossover operation has not been introduced so far. Here, we introduce a novel quantum genetic algorithm which has a quantum crossover procedure performing crossovers among all chromosomes in parallel for each generation. A complexity analysis shows that a quadratic speedup is achieved over its classical counterpart in the dominant factor of the run time to handle each generation.Comment: 21 pages, 1 table, v2: typos corrected, minor modifications in sections 3.5 and 4, v3: minor revision, title changed (original title: Semiclassical genetic algorithm with quantum crossover and mutation operations), v4: minor revision, v5: minor grammatical corrections, to appear in QI

Optimal advertising campaign generation for multiple brands using MOGA

The paper proposes a new modified multiobjective genetic algorithm (MOGA) for the problem of optimal television (TV) advertising campaign generation for multiple brands. This NP-hard combinatorial optimization problem with numerous constraints is one of the key issues for an advertising agency when producing the optimal TV mediaplan. The classical approach to the solution of this problem is the greedy heuristic, which relies on the strength of the preceding commercial breaks when selecting the next break to add to the campaign. While the greedy heuristic is capable of generating only a group of solutions that are closely related in the objective space, the proposed modified MOGA produces a Pareto-optimal set of chromosomes that: 1) outperform the greedy heuristic and 2) let the mediaplanner choose from a variety of uniformly distributed tradeoff solutions. To achieve these results, the special problem-specific solution encoding, genetic operators, and original local optimization routine were developed for the algorithm. These techniques allow the algorithm to manipulate with only feasible individuals, thus, significantly improving its performance that is complicated by the problem constraints. The efficiency of the developed optimization method is verified using the real data sets from the Canadian advertising industry

A self-organizing random immigrants genetic algorithm for dynamic optimization problems

This is the post-print version of the article. The official published version can be obtained from the link below - Copyright @ 2007 SpringerIn this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimization problems. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case with small diversity, one single replacement can affect a large number of individuals in the population. This simple approach can take the system to a self-organizing behavior, which can be useful to control the diversity level of the population and hence allows the genetic algorithm to escape from local optima once the problem changes due to the dynamics.This work was supported by FAPESP (Proc. 04/04289-6)

Comparing and Combining Lexicase Selection and Novelty Search

Lexicase selection and novelty search, two parent selection methods used in evolutionary computation, emphasize exploring widely in the search space more than traditional methods such as tournament selection. However, lexicase selection is not explicitly driven to select for novelty in the population, and novelty search suffers from lack of direction toward a goal, especially in unconstrained, highly-dimensional spaces. We combine the strengths of lexicase selection and novelty search by creating a novelty score for each test case, and adding those novelty scores to the normal error values used in lexicase selection. We use this new novelty-lexicase selection to solve automatic program synthesis problems, and find it significantly outperforms both novelty search and lexicase selection. Additionally, we find that novelty search has very little success in the problem domain of program synthesis. We explore the effects of each of these methods on population diversity and long-term problem solving performance, and give evidence to support the hypothesis that novelty-lexicase selection resists converging to local optima better than lexicase selection

Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes for (sub-)fitness evaluation purposes are examined for two multiple-choice optimisation problems. It is shown that random partnering strategies perform best by providing better sampling and more diversity

'On the Application of Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners'

This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the sub-populations on solution quality are examined for two constrained optimisation problems. We examine a number of recombination partnering strategies in the construction of higher-level individuals and a number of related schemes for evaluating sub-solutions. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements