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Genetic Algorithms

Authors:
Sandeep Kumar at Christ University, Bangalore
Sandeep Kumar
Harish Sharma at Rajasthan Technical University
Harish Sharma
Er. Sanjay Jain at Amity University
Er. Sanjay Jain
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Abstract

Nature always provides some of the well-planned way to solve the real-life problems. Science is a conversation between the scientists/researchers and nature. The study of nature has progressed for thousands of years, advancing with innovative models, techniques, and tools and has established well-defined disciplines of scientific goals. Humankind has continuously attempted to understand nature by evolving innovative techniques and tools day by day. Genetic algorithms are regarded as the most popular technique in evolutionary algorithms. They mimic Charles Darwin’s principle of natural evolution. This chapter will focus on the growing area of genetic algorithms. The purpose is to present an in-depth analysis of genetic algorithms. Genetic algorithms are being utilized as adaptive algorithms for solving real-world problems and as a unique computational model of natural evolutionary systems. The chapter will give in-depth overview of genetic optimization, enabling researchers to derive simple genetic algorithms, and will also highlight the pros and cons of genetic algorithms.
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Genetic Algorithms
Sandeep Kumar, Sanjay Jain, and Harish Sharma
CONTENTS
2.1 Overview of Genetic Algorithms. . .............................27
2.2 Genetic Optimization . .......................................32
2.2.1 Global Optimization ..............................33
2.2.2 Constrained Optimization ..........................34
2.2.3 Combinatorial Optimization ........................37
2.2.4 Multi-Objective Optimization........................38
2.3 Derivation of Simple Genetic Algorithm ........................39
2.4 Genetic Algorithms vs. Other Optimization
Techniques .................................................43
2.5 Pros and Cons of Genetic Algorithms. . . ........................45
2.6 Hybrid Genetic Algorithms . ..................................45
2.7 Possible Applications of Computer Science via Genetic
Algorithms..................................................46
2.8 Conclusion .................................................47
2.1 Overview of Genetic Algorithms
Nature-inspired computing is a combination of computing science and the
knowledge from different academic streams, such as mathematics, biology,
chemistry, physics, and engineering. This diversity inspires the researchers to
develop innovative computational tools for hardware, software, or wetware
and for the synthesis of patterns, behaviours, and organisms, to get rid of
complex problems. The algorithms simulating processes in nature or inspired
from some natural phenomenon are called nature-inspired algorithms.
According to the source of inspiration, they are divided into different classes.
Most of the nature-inspired algorithms are inspired by the working of natural
biological systems, swarm intelligence, and physical and chemical phenom-
ena. For that reason, they are called biology based, swarm intelligence based,
27
spaces, even those applied in supercomputers. The GA is a vigorous search
technique necessitating pint-sized knowledge to achieve search efficiently
in huge search spaces or those where the reason is poorly understood.
Specifically, the GA progresses through a population of points in contrast
to the single point of focus of most search algorithms. Furthermore, it is
valuable in the highly complicated region of non-linear problems. Its
inherent parallelism (in evaluation functions, selections, and so on) permits
the use of distributed processing machines. In GA, the selection of good
parameter settings that work for a specific problem is very simple. It may
be concluded that GA is the best choice for multi-objective optimization
and applicable for a large class of problems.
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Full-text available
  • Nov 2014
The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.
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Automated docking using Lamarckian genetic algorithm and an empirical binding free energy function
Article
  • Nov 1998
  • J COMPUT CHEM
A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon binding. Interestingly, this method applies a Lamarckian model of genetics, in which environmental adaptations of an individual's phenotype are reverse transcribed into its genotype and become heritable traits (sic). We consider three search methods, Monte Carlo simulated annealing, a traditional genetic algorithm, and the Lamarckian genetic algorithm, and compare their performance in dockings of seven protein–ligand test systems having known three-dimensional structure. We show that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckian genetic algorithm is the most efficient, reliable, and successful of the three. The empirical free energy function was calibrated using a set of 30 structurally known protein–ligand complexes with experimentally determined binding constants. Linear regression analysis of the observed binding constants in terms of a wide variety of structure-derived molecular properties was performed. The final model had a residual standard error of 9.11 kJ mol⁻¹ (2.177 kcal mol⁻¹) and was chosen as the new energy function. The new search methods and empirical free energy function are available in AUTODOCK, version 3.0. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1639–1662, 1998
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Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing
Article
  • Jul 2016
Recent expansions of Internet-of-Things (IoT) applying cloud computing have been growing at a phenomenal rate. As one of the developments, heterogeneous cloud computing has enabled a variety of cloud-based infrastructure solutions, such as multimedia big data. Numerous prior researches have explored the optimizations of on-premise heterogeneous memories. However, the heterogeneous cloud memories are facing constraints due to the performance limitations and cost concerns caused by the hardware distributions and manipulative mechanisms. Assigning data tasks to distributed memories with various capacities is a combinatorial NP-hard problem. This paper focuses on this issue and proposes a novel approach, Cost-Aware Heterogeneous Cloud Memory Model (CAHCM), aiming to provision a high performance cloud-based heterogeneous memory service offerings. The main algorithm supporting CAHCM is Dynamic Data Allocation Advance (2DA) Algorithm that uses genetic programming to determine the data allocations on the cloud-based memories. In our proposed approach, we consider a set of crucial factors impacting the performance of the cloud memories, such as communication costs, data move operating costs, energy performance, and time constraints. Finally, we implement experimental evaluations to examine our proposed model. The experimental results have shown that our approach is adoptable and feasible for being a cost-aware cloud-based solution.
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Improved Test Pattern Generation for Hardware Trojan Detection Using Genetic Algorithm and Boolean Satisfiability
Conference Paper
  • Sep 2015
Test generation for Hardware Trojan Horses (HTH) detection is extremely challenging, as Trojans are designed to be triggered by very rare logic conditions at internal nodes of the circuit. In this paper, we propose a Genetic Algorithm (GA) based Automatic Test Pattern Generation (ATPG) technique, enhanced by automated solution to an associated Boolean Satisfiability problem. The main insight is that given a specific internal trigger condition, it is not possible to attack an arbitrary node (payload) of the circuit, as the effect of the induced logic malfunction by the HTH might not get propagated to the output. Based on this observation, a fault simulation based framework has been proposed, which enumerates the feasible payload nodes for a specific triggering condition. Subsequently, a compact set of test vectors is selected based on their ability to detect the logic malfunction at the feasible payload nodes, thus increasing their effectiveness. Test vectors generated by the proposed scheme were found to achieve higher detection coverage over large population of HTH in ISCAS benchmark circuits, compared to a previously proposed logic testing based Trojan detection technique.
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Selecting Optimal Conditioning Factors in Shallow Translational Landslide Susceptibility Mapping Using Genetic Algorithm
Article
  • Apr 2015
  • ENG GEOL
Many landslide conditioning factors have been considered in the literature for landslide susceptibility mapping, but it is not certain which factors produce the best result for an area under analysis. With the availability of increasing number of landslide conditioning factors, finding the best combination of factors has become an important research issue. In this study, genetic algorithms (GA) were applied to find the best factors combination among 16 factors available for the study area, Macka District of Trabzon, Turkey. Performances of the models including 4 to 15 factors were evaluated using logistic regression to investigate the effect of varying number of factors and the most effective factors were determined. Results showed that prediction accuracy of the models constructed with GA-selected factors increased to a certain level (up to 8 factors) and then showed a stable trend producing statistically similar results. In order to show the robustness of the GA algorithm, prediction performance of the models constructed with 4 to 8 factors determined by the GA were compared with those of models constructed with factor combinations applied in the literature. While slope, lithology and distance to drainage were found to be the most effective factors, soil depth, slope length and profile curvature were found to be the least effective ones. It was also found that the models determined by the GA generally produced better results than user’s models. Also, the goodness of the GA-based models was confirmed by success rate curve analysis and McNemar’s test. In summary, results revealed the robustness of the GA when searching the optimal landslide conditioning factors among a large number of factors.
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Distributed Generation Allocation on Radial Distribution Networks Under Uncertainties of Load and Generation Using Genetic Algorithm
Article
  • Apr 2015
This paper presents a distribution generation (DG) allocation strategy for radial distribution networks under uncertainties of load and generation using adaptive genetic algorithm (GA). The uncertainties of load and generation are modeled using fuzzy-based approach. The optimal locations for DG integration and the optimal amount of generation for these locations are determined by minimizing the network power loss and maximum node voltage deviation. Since GA is a metaheuristic algorithm, the results of multiple runs are taken and the statistical variations for locations and generations for DG units are shown. The locations and sizes for DG units obtained with fuzzy-based approach are found to be different than those obtained with deterministic approach. The results obtained with fuzzy-based approach are found to be comparatively efficient in working with future load growth. The proposed approach is demonstrated on the IEEE 33-node test network and a 52-node Indian practical distribution network.
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