Modelling Autism Spectrum Disorders: An Optimization Approach with Application

Ibrahim Mohammed Sulaiman, Maulana Malik, Norsuhaily Abu Bakar, Hasni Hassan, Mustafa Mamat, Zulfa Izza Hashim


Autism spectrum disorders (ASDs) refer to a group of neurodevelopmental disorders that can cause significant behavioral, communication, and social challenges. Since the biological causes are genetic, the genes responsible for the causes of autism are still yet to be identified. In Malaysia, approximately 9,000 children are born with autism yearly. However, to date, no study has been done to model this disorder through an optimization approach. Hence, modeling autism spectrum disorders using an optimization method may lead to new research in this area. Therefore, this paper proposes a new hybrid conjugate gradient method for solving unconstrained optimization problems and an autistic regression model. The authors parameterized cases of autistic children to construct an autistic optimization model and apply the Hybrid Conjugate Gradient (HCG) method to seek the solution. The hybrid HCG method is an efficient optimization algorithm. It helps to solve large-scale unconstrained optimization problems due to its low memory requirement and excellent convergence results. Additionally, it features efficient numerical performance. The authors established the convergence analysis of the proposed method under some suitable conditions. The authors further extended the proposed method to solve the problem of portfolio selection. The authors prove that the hybrid HCG optimization model efficiently solves large-scale unconstrained optimization problems based on the preliminary results.

Keywords: autism spectrum disorders, Malaysia, unconstrained optimization, convergence analysis, line search.

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