Microarray technology can be used as an efficient diagnostic system to recognise diseases such as tumours or to discriminate between different types of cancers in normal tissues. This technology has received increasing attention from the bioinformatics community because of its potential in designing powerful decision-making tools for cancer diagnosis. However, the presence of thousands or tens of thousands of genes affects the predictive accuracy of this technology from the perspective of classification. Thus, a key issue in microarray data is identifying or selecting the smallest possible set of genes from the input data that can achieve good predictive accuracy for classification. In this work, we propose a two-stage selection algorithm for gene selection problems in microarray data-sets called the symmetrical uncertainty filter and harmony search algorithm wrapper SU-HSA. Experimental results show that the SU-HSA is better than HSA in isolation for all data-sets in terms of the accuracy and achieves a lower number of genes on 6 out of 10 instances. Furthermore, the comparison with state-of-the-art methods shows that our proposed approach is able to obtain 5 out of 10 new best results in terms of the number of selected genes and competitive results in terms of the classification accuracy.

Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm

Harmony Search (HS) is a metaheuristic optimisation algorithm inspired by musical improvisation. So far it has been applied to various optimisation problems, and there are several application-oriented review papers. However, this review paper tries to focus on the historical development of algorithm structure instead of applications. This paper explains the original HS algorithm along with a selection of modified and hybrid HS methods: adaption of original operators of the basic harmony search, parameter adaption, hybrid methods, handling multi-objective optimisation problems and constraint handling.

Review of harmony search with respect to algorithm structure

A self-adaptive harmony particle swarm optimization search algorithm is proposed.PSO algorithm is utilized to initial the harmony memory (HM).Pitch adjusting rate (PAR) and distance bandwidth (bw), are adjusted dynamically.A Gaussian mutation operator is added to reinforce the robustness.The convergence of the SHPSOS algorithm has been proved theoretically. Harmony Search (HS) algorithm is a new population-based meta-heuristic which imitates the music improvisation process and has been successfully applied to a variety of combination optimization problems. In this paper, a self-adaptive harmony particle swarm optimization search algorithm, named SHPSOS, is proposed to solve global continuous optimization problems. Firstly, an efficient initialization scheme based on the PSO algorithm is presented for improving the solution quality of the initial harmony memory (HM). Secondly, a new self-adaptive adjusting scheme for pitch adjusting rate (PAR) and distance bandwidth (BW), which can balance fast convergence and large diversity during the improvisation step, are designed. PAR is dynamically adapted by symmetrical sigmoid curve, and BW is dynamically adjusted by the median of the harmony vector at each generation. Meanwhile, a new effective improvisation scheme based on differential evolution and the best harmony (best individual) is developed to accelerate convergence performance and to improve solution accuracy. Besides, Gaussian mutation strategy is presented and embedded in the SHPSOS algorithm to reinforce the robustness and avoid premature convergence in the evolution process of candidates. Finally, the global convergence performance of the SHPSOS is analyzed with the Markov model to testify the stability of algorithm. Experimental results on thirty-two standard benchmark functions demonstrate that SHPSOS outperforms original HS and the other related algorithms in terms of the solution quality and the stability.

A self-adaptive harmony PSO search algorithm and its performance analysis

The island model is one of the most well-known structured population strategies used to control the diversity in evolutionary algorithms. The population of an island-based evolutionary algorithm is normally partitioned into several sub-populations (islands). An evolutionary
algorithm is then applied to each island independently. After a number of predefined generations, a migration process takes place to exchange specific candidate solutions between the islands. Recently, the Cuckoo search (CS) algorithm has been proposed as a population-based algorithm that mimics the nesting and parasitic reproduction behaviors of some cuckoo species. The main drawback of the CS algorithm is that its evolutionary operators may not adequately preserve the diversity of its population during the evolution process which may cause it to converge earlier than expected to suboptimal solutions. This paper introduces an improved variation of CS called island-based CS with polynomial mutation (iCSPM) that adapts two improvements to CS. First, the strategy of island model is incorporated into the CS algorithm to empower its capability in controlling the diversity of its population. Second, the L´evy flight method in CS is replaced with the highly disruptive polynomial mutation method in an attempt to enhance the exploration of CS. The iCSPM algorithm was evaluated using 15 standard benchmark functions in terms of the accuracy and reliability of the obtained results over multiple simulations. The sensitivity analysis of the main parameters of iCSPM was carried out to show their effect on the convergence behavior of iCSPM. The experimental results suggest that iCSPM provides more accurate and reliable results than 3 competitive methods. The source code of iCSPM is available at https://www.dropbox.com/sh/99x5374fiz2e390/AAC_6Eb9VFrDEdvt6wAmJstSa?dl=0

Island-based Cuckoo Search with Highly Disruptive Polynomial Mutation

Medicine has, until recently, been slow to adapt to information technologies and systems for many reasons, but the future lies therein. Innovations in Data Methodologies and Computational Algorithms for Medical Applications offers the most cutting-edge research in the field, offering insights into case studies and methodologies from around the world. The text details the latest developments and will serve as a vital resource to practitioners and academics alike in the burgeoning field of medical applications of technologies. As security and privacy improve, Electronic Health Records and informatics in the medical field are becoming ubiquitous, and staying abreast of the latest information can be difficult. This volume serves as a reference handbook and theoretical framework for the future of the field.

Innovations in Data Methodologies and Computational Algorithms for Medical Applications

Hybridizing Harmony Search algorithm with different mutation operators for continuous problems

Evaluating the Effectiveness of Mutation Operators on the Behavior of Genetic Algorithms Applied to Non-deterministic Polynomial Problems

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Harmony search algorithm (HSA) is a recent evolutionary algorithm used to solve several optimization problems. The algorithm mimics the improvisation behaviour of a group of musicians to find a good harmony. Several variations of HSA have been proposed to enhance its performance. In this paper, a new variation of HSA that uses multi-parent crossover is proposed (HSA-MPC). In this technique three harmonies are used to generate three new harmonies that will replace the worst three solution vectors in the harmony memory (HM). The algorithm has been applied to solve a set of eight real world numerical optimization problems (1-8) introduced for IEEE-CEC2011 evolutionary algorithm competition. The experimental results of the proposed algorithm are compared with the original HSA, and two variations of HSA: global best HSA and tournament HSA. The HSA-MPC almost always shows superiority on all test problems.

Hybridizing Harmony Search Algorithm with Multi-Parent Crossover to Solve Real World Optimization Problems

Artificial Bee Colony (ABC) is a swarm-based metaheuristic for continuous optimization. Recent work hybridized this algorithm with other metaheuristics in order to improve performance. The work in this paper, experimentally evaluates the use of different mutation operators with the ABC algorithm. The introduced operator is activated according to a determined probability called mutation rate (MR). The results on standard benchmark function suggest that the use of this operator improves performance in terms of convergence speed and quality of final obtained solution. It shows that Power and Polynomial mutations give best results. The fastest convergence was for the mutation rate value (MR=0.2).

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Artificial Bee Colony with Different Mutation Schemes - A comparative study.

Genetic Algorithms (GAs) are powerful general-purpose optimization search algorithms based upon the principles of evolution observed in nature. Mutation operator is one of the GA operators that used to produce new chromosomes or modify some features of it depending on some small probability value. The objective of this operator is to prevent falling of all solutions in population into a local optimum of solved problem. This paper applies several mutation methods to different non-deterministic polynomial (NP) hard problems and compares the results. The problems that will be introduced in this paper are: traveling salesman problem (TSP), 0/1 Knapsack problem, and Shubert function.

Basima Hani F. Hasan

Papers

Hybridizing Harmony Search algorithm with different mutation operators for continuous problems

Evaluating the Effectiveness of Mutation Operators on the Behavior of Genetic Algorithms Applied to Non-deterministic Polynomial Problems

Hybridizing Harmony Search Algorithm with Multi-Parent Crossover to Solve Real World Optimization Problems

Artificial Bee Colony with Different Mutation Schemes - A comparative study.

Comparative Study of Mutation Operators on the Behavior of Genetic Algorithms Applied to Non-deterministic Polynomial (NP) Problems