Çörüş, Doğan

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Name Variants
Corus,D.
Corus, Dogan
Corus,Dogan
C.,Dogan
DOĞAN ÇÖRÜŞ
D. Çörüş
ÇÖRÜŞ, Doğan
Doğan Çörüş
Ç., Doğan
Dogan, Corus
C., Dogan
Çörüş D.
Çörüş, D.
D. Cörüş
ÇÖRÜŞ, DOĞAN
Doğan ÇÖRÜŞ
Çörüş,D.
Çörüş, Doğan
Cörüş, Doğan
Cörüş, D.
Doğan Cörüş
Çörüş, DOĞAN
Job Title
Dr. Öğr. Üyesi
Email Address
dogan.corus@khas.edu.tr
Main Affiliation
Computer Engineering
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Website
ORCID ID
0000-0003-4151-2734
Scopus Author ID
55837546200
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
ABA-7118-2021
Scholarly Output

2

Articles

1

Citation Count

17

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Now showing 1 - 2 of 2
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    Citation - WoS: 10
    Citation - Scopus: 11
    Automatic Adaptation of Hypermutation Rates for Multimodal Optimisation
    (Assoc Computing Machinery, 2021) Çörüş, Doğan; Oliveto, Pietro S.; Yazdani, Donya
    Previous work has shown that in Artificial Immune Systems (AIS) the best static mutation rates to escape local optima with the ageing operator are far from the optimal ones to do so via large hypermutations and vice-versa. In this paper we propose an AIS that automatically adapts the mutation rate during the run to make good use of both operators. We perform rigorous time complexity analyses for standard multimodal benchmark functions with significant characteristics and prove that our proposed algorithm can learn to adapt the mutation rate appropriately such that both ageing and hypermutation are effective when they are most useful for escaping local optima. In particular, the algorithm provably adapts the mutation rate such that it is efficient for the problems where either operator has been proven to be effective in the literature.
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    Article
    Citation - WoS: 14
    Citation - Scopus: 14
    Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation
    (Institute of Electrical and Electronics Engineers Inc., 2021) Çörüş, Doğan; Oliveto, Pietro Simone; Yazdani, Donya
    Various studies have shown that immune system inspired hypermutation operators can allow artificial immune systems (AIS) to be very efficient at escaping local optima of multimodal optimisation problems. However, this efficiency comes at the expense of considerably slower runtimes during the exploitation phase compared to standard evolutionary algorithms. We propose modifications to the traditional ‘hypermutations with mutation potential’ (HMP) that allow them to be efficient at exploitation as well as maintaining their effective explorative characteristics. Rather than deterministically evaluating fitness after each bit-flip of a hypermutation, we sample the fitness function stochastically with a ‘parabolic’ distribution. This allows the ‘stop at first constructive mutation’ (FCM) variant of HMP to reduce the linear amount of wasted function evaluations when no improvement is found to a constant. The stochastic distribution also allows the removal of the FCM mechanism altogether as originally desired in the design of the HMP operators. We rigorously prove the effectiveness of the proposed operators for all the benchmark functions where the performance of HMP is rigorously understood in the literature. We validate the gained insights to show linear speed-ups for the identification of high quality approximate solutions to classical NP-Hard problems from combinatorial optimisation. We then show the superiority of the HMP operators to the traditional ones in an analysis of the complete standard Opt-IA AIS, where the stochastic evaluation scheme allows HMP and ageing operators to work in harmony. Through a comparative performance study of other ‘fast mutation’ operators from the literature, we conclude that a power-law distribution for the parabolic evaluation scheme is the best compromise in black-box scenarios where little problem knowledge is available.