Gitanshu Behal

Gitanshu Behal

Noida, Uttar Pradesh, India
7K followers 500+ connections

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About

Passionate full stack Software Engineer with around 10 years of experience in domains…

Activity

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Experience

  • Microsoft Graphic

    Microsoft

    Noida, Uttar Pradesh, India

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    Noida, Uttar Pradesh, India

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    Noida, Uttar Pradesh, India

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    Hyderabad, Telangana, India

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    Hyderabad, Telangana, India

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    Hyderabad, Telangana, India

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    Hyderabad, Telangana, India

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    Gurgaon, India

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    Gurugram, Haryana, India

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    Gurgaon, India

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    Gurugram, Haryana, India

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    Gurugram, Haryana, India

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    Work from Home

Education

  • Delhi College of Engineering Graphic

    Delhi Technological University

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    Activities and Societies: Dce Coders - The programming group of our college, DCE Programmers, IEEE Society, Graphic Designing, 3D Animation.

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Licenses & Certifications

Publications

  • On the Applicability of Diploid Genetic Algorithms in Dynamic Environments

    Springer (ISCMI2014)

    Diploidity is the essence of the nature. However, it has largely been ignored by the computer science fraternity. Simple Genetic Algorithms and their variants have extensively been used in solving NP hard problems in-spite of the fact that Diploid Genetic Algorithms assure robustness as against Simple Genetic Algorithms which solitary guarantee optimization. Moreover, the past endeavors proved that these algorithms are more successful in dynamic environments as compared to their haploid…

    Diploidity is the essence of the nature. However, it has largely been ignored by the computer science fraternity. Simple Genetic Algorithms and their variants have extensively been used in solving NP hard problems in-spite of the fact that Diploid Genetic Algorithms assure robustness as against Simple Genetic Algorithms which solitary guarantee optimization. Moreover, the past endeavors proved that these algorithms are more successful in dynamic environments as compared to their haploid counterpart. The work proves the above point by applying Diploid genetic Algorithms to Dynamic Travelling Salesman Problem and comparing the results to Greedy Approach and Simple Genetic Algorithms. The works also presents a hybrid approach namely Greedy Genetic Approach. The results of the experiments established that diploidity ensures robustness. In the experiments carried out, the three variants of dominance were implemented and 115 trials bought forth the point that though Haploid and Greedy Approaches do not outperform the other, Diploid are the best bet for dynamic environments.

    See publication

  • On the Applicability of Diploid Genetic Algorithms in Dynamic Environments

    IEEEXplore (ISCMI2014)

    Diploidity is the essence of the nature. However, it has largely been ignored by the computer science fraternity. Simple Genetic Algorithms and their variants have extensively been used in solving NP hard problems in-spite of the fact that Diploid Genetic Algorithms assure robustness as against Simple Genetic Algorithms which solitary guarantee optimization. Moreover, the past endeavors proved that these algorithms are more successful in dynamic environments as compared to their haploid…

    Diploidity is the essence of the nature. However, it has largely been ignored by the computer science fraternity. Simple Genetic Algorithms and their variants have extensively been used in solving NP hard problems in-spite of the fact that Diploid Genetic Algorithms assure robustness as against Simple Genetic Algorithms which solitary guarantee optimization. Moreover, the past endeavors proved that these algorithms are more successful in dynamic environments as compared to their haploid counterpart. The work proves the above point by applying Diploid genetic Algorithms to Dynamic Travelling Salesman Problem and comparing the results to Greedy Approach and Simple Genetic Algorithms. The works also presents a hybrid approach namely Greedy Genetic Approach. The results of the experiments established that diploidity ensures robustness. In the experiments carried out, the three variants of dominance were implemented and 115 trials bought forth the point that though Haploid and Greedy Approaches do not outperform the other, Diploid are the best bet for dynamic environments.

    Other authors
    See publication

  • Opimized Solution to NP-Copmlete 'Maximum Clique Problem' using Genetic Algorithms

    IOTA-The Annual Technical Journal of IEEE DTU, vol. XIX

    The Maximum Clique Problem is an NP-Complete problem which has diversified applications in technical fields. Here, we propose a solution to this problem using Genetic Algorithms (GAs).The work suggests that natural biological concepts like inheritance and evolution can be implemented very efficiently to solve problems in computer science.

    Other authors

Projects

  • Applicability of BAT algorithm on Dynamic Travelling Salesman Problem and its optimization using Hadoop

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    > Proposed the modifications in BAT algorithm to use it for discrete optimizations like Dynamic travelling salesman problem.
    > Optimized the algorithm using MapReduce architecture of Hadoop.
    > Compared the results with Greedy, Simple Genetic Algorithm and Diploid Genetic Algorithm.
    > Implemented the code in Java.

    Other creators

  • Internet of Things

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    Implementation of Voice Recognition based Home Automation console that is easily accessible from distant places via Facebook application through a simple web server running inside the home.

    > Developed Home Automation System controlled by Facebook Application.
    > Used Arduino LAN shield to communicate with the Web server.
    > Used PHP for scripting,

    Other creators
    See project

  • AI Chess Playing Bot

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    > Developed central interface of Chess following all the rules of Chess.
    > Developed BOTS of different level of difficulty using algorithms like greedy, Min-max and alpha-beta pruning.

    Other creators
    See project

  • Diploid Genetics Algorithm Application on Dynamic Environment

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    I implemented the DGA algorithm in C++. Also, suggested a new dominance scheme for converting genotypes to phenotypes in DGA.

    Other creators
    See project

  • AI Bot playing Othello

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    Coded for the competition - BOTS, The Artificial Intelligence Gaming Challenge: Here, the problem statement was to create a bot that can play the game Reversi (also called, othello).

    > Coded the bot using minimax using C++.
    > The depth of the algorithm was 6.
    > Our team was the runner ups in the contest.

    Other creators

Test Scores

  • AIEEE

    Score: 1649-AIR

Languages

  • English

    Native or bilingual proficiency

  • Hindi

    Native or bilingual proficiency

Organizations

  • IEEE DTU

    Student Member

    - Present

  • Cisco

    Winter Intern - Online

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