UH Biocomputation Group - Fitness landscape analysishttp://biocomputation.herts.ac.uk/2021-03-17T16:33:00+00:00How to Exploit Fitness Landscape Properties of Timetabling Problem: A New Operator for Quantum Evolutionary Algorithm2021-03-17T16:33:00+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 "How to Exploit Fitness Landscape Properties of Timetabling Problem: A New Operator for Quantum Evolutionary Algorithm"</p>
<p>This week on Journal Club session Mohammad Hassan Tayarani Najaran will talk about a paper "How to Exploit Fitness Landscape Properties of Timetabling Problem: A New Operator for Quantum Evolutionary Algorithm".</p>
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<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>
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<p>Papers:</p>
<ul class="simple">
<li>M. Najaran, <a class="reference external" href="https://doi.org/10.1016/j.eswa.2020.114211">"How to Exploit Fitness Landscape Properties of Timetabling Problem: A New Operator for Quantum Evolutionary Algorithm"</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>