http://biocomputation.herts.ac.uk/2021-03-17T16:33:00+00:002021-03-17T16:33:00+00:002021-03-17T16:33:00+00:00Mohammad Hassan Tayarani Najarantag:biocomputation.herts.ac.uk,2021-03-17:/2021/03/17/how-to-exploit-fitness-landscape-properties-of-timetabling-problem-a-new-operator-for-quantum-evolutionary-algorithm.html

<p class="first last">Mohammad Hassan Tayarani Najaran's Journal Club session where he will talk about a paper &quot;How to Exploit Fitness Landscape Properties of Timetabling Problem: A New Operator for Quantum Evolutionary Algorithm&quot;</p>

<p>This week on Journal Club session Mohammad Hassan Tayarani Najaran will talk about a paper &quot;How to Exploit Fitness Landscape Properties of Timetabling Problem: A New Operator for Quantum Evolutionary Algorithm&quot;.</p> <hr class="docutils" /> <p>The fitness landscape of the timetabling problems is analyzed in this paper to provide some insight into the properties of the problem. The analyses suggest that the good solutions are clustered in the search space and there is a correlation between the fitness of a local optimum and its distance to the best solution. Inspired by these findings, a new operator for Quantum Evolutionary Algorithms is proposed which, during the search process, collects information about the fitness landscape and tried to capture the backbone structure of the landscape. The knowledge it has collected is used to guide the search process towards a better region in the search space. The proposed algorithm consists of two phases. The first phase uses a tabu mechanism to collect information about the fitness landscape. In the second phase, the collected data are processed to guide the algorithm towards better regions in the search space. The algorithm clusters the good solutions it has found in its previous search process. Then when the population is converged and trapped in a local optimum, it is divided into sub-populations and each sub-population is designated to a cluster. The information in the database is then used to reinitialize the q-individuals, so they represent better regions in the search space. This way the population maintains diversity and by capturing the fitness landscape structure, the algorithm is guided towards better regions in the search space. The algorithm is compared with some state-of-the-art algorithms from PATAT competition conferences and experimental results are presented.</p> <div class="line-block"> <div class="line"><br /></div> </div> <p>Papers:</p> <ul class="simple"> <li>M. Najaran, <a class="reference external" href="https://doi.org/10.1016/j.eswa.2020.114211">&quot;How to Exploit Fitness Landscape Properties of Timetabling Problem: A New Operator for Quantum Evolutionary Algorithm&quot;</a>, 2021, Expert Systems with Applications, 168, 114211</li> </ul> <p><strong>Date:</strong> 2021/03/17 <br /> <strong>Time:</strong> 14:00 <br /> <strong>Location</strong>: online</p> 2020-06-17T13:30:28+01:002020-06-17T13:30:28+01:00Emil Dmitruktag:biocomputation.herts.ac.uk,2020-06-17:/2020/06/17/probabilistic-optimization-algorithms-for-real-coded-problems-and-its-application-in-latin-hypercube-problem.html

<p class="first last">Mohammad Tayarani-Najaran's journal club session where he will talk about the his paper &quot;Probabilistic Optimization Algorithms for Real-Coded Problems And Its Application in Latin Hypercube Problem&quot;.</p>

<p>This week on Journal Club session Mohammad Tayarani-Najaran will talk about paper &quot;Probabilistic Optimization Algorithms for Real-Coded Problems And Its Application in Latin Hypercube Problem&quot;.</p> <hr class="docutils" /> <p>This paper proposes a novel optimization algorithm for read-coded problems called the Probabilistic Optimization Algorithm (POA). In the proposed algorithm, rather than a binary or integer, a probabilistic representation is used for the individuals. Each individual in the proposed algorithm is a probability density function and is capable of representing the entire search space simultaneously. In the search process, each solution performs as a local search and climbs the local optima, and at the same time, the interaction among the probabilistic individuals in the population offers a global search. The parameters of the proposed algorithm are studied in this paper and their effect on the search process is presented. A structured population is proposed for the algorithm and the effect of different structures is analyzed. The algorithm is used to solve Latin Hyper-cube problem and experimental studies suggest promising results. Different benchmark functions are also used to test the algorithm and results are presented. The analyses suggest that the improvement is more significant for large scale problems.</p> <p>Papers:</p> <ul class="simple"> <li>Mohammad Hassan Tayarani Najaran, Mohammad Reza Akbarzadeh Tootounchi, <a class="reference external" href="http://www.sciencedirect.com/science/article/pii/S0957417420304139">&quot;Probabilistic Optimization Algorithms for Real-Coded Problems And Its Application in Latin Hypercube Problem&quot;</a> , Expert Systems with Applications, 2020, 113589, ISSN 0957-4174, <a class="reference external" href="https://doi.org/10.1016/j.eswa.2020.113589">https://doi.org/10.1016/j.eswa.2020.113589</a>.</li> </ul> <div class="line-block"> <div class="line"><br /></div> </div> <p><strong>Date:</strong> 19/06/2020 <br /> <strong>Time:</strong> 16:00 <br /> <strong>Location</strong>: online</p>