Momodou L. Sanyang

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We consider Black-Box continuous optimization by Estimation of Distribution Algorithms (EDA). In continuous EDA, the multivariate Gaussian distribution is widely used as a search operator, and it has the well-known advantage of modelling the correlation structure of the search variables, which univariate EDA lacks. However, the Gaussian distribution as a(More)
In this paper, we present a new variant of EDA for high dimensional continuous optimisation, which extends a recently proposed random projections (RP) ensemble based approach by employing heavy tailed random matrices. In particular, we use random matrices with i.i.d. t-distributed entries. The use of t-distributions may look surprising in the context of(More)
We consider the problem of high dimensional black-box optimisation via Estimation of Distribution Algorithms (EDA). The Gaussian distribution is commonly used as a search operator in most of the EDA methods. However there are indications in the literature that heavy tailed distributions may perform better due to their higher exploration capabilities.(More)
It has been observed that in many real-world large scale problems only few variables have a major impact on the function value: While there are many inputs to the function, there are just few degrees of freedom. We refer to such functions as having a low intrinsic dimension. In this paper we devise an Estimation of Distribution Algorithm (EDA) for(More)
A major surface protein complex on the Plasmodium falciparum merozoite, Merozoite Surface Protein-1 (MSP-1) undergoes a proteolytic cleavage at the time of erythrocyte invasion by the parasite. Two murine monoclonal antibodies, mAb 12.8 and mAb 12.10, are specific for a 19 kDa subunit of MSP-1 (MSP-119) and can prevent both this proteolytic cleavage and(More)
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