2 research outputs found
Recommending Healthy Meal Plans by Optimising Nature-Inspired Many-Objective Diet Problem
Healthy eating is an important issue affecting a large part of the world population, so human diets are
becoming increasingly popular, especially with the devastating consequences of Coronavirus Disease
(Covid-19). A realistic and sustainable diet plan can help us to have a healthy eating habit since it considers
most of the expectations from a diet without any restriction. In this study, the classical diet problem has
been extended in terms of modelling, data sets and solution approach. Inspired by animals’ hunting strategies,
it was re-modelled as a many-objective optimisation problem. In order to have realistic and applicable diet
plans, cooked dishes are used. A well-known many-objective evolutionary algorithm is used to solve the
diet problem. Results show that our approach can optimise specialised daily menus for different user types,
depending on their preferences, age, gender and body index. Our approach can be easily adapted for users
with health issues by adding new constraints and objectives. Our approach can be used individually or by
dietitians as a decision support mechanism
Comparison of evolutionary techniques for Value-at-Risk calculation
The Value-at-Risk (VaR) approach has been used for measuring and controlling the market risks in financial institutions. Studies show that the t-distribution is more suited to representing the financial asset returns in VaR calculations than the commonly used normal distribution. The frequency of extremely positive or extremely negative financial asset returns is higher than that is suggested by normal distribution. Such a leptokurtic distribution can better be approximated by a t-distribution. The aim of this study is to asses the performance of a real coded Genetic Algorithm (CA) with Evolutionary Strategies (ES) approach for Maximum Likelihood (ML) parameter estimation. Using Monte Carlo (MC) simulations, we compare the test results of VaR simulations using the t-distribution, whose optimal parameters are generated by the Evolutionary Algorithms (EAs), to that of the normal distribution. It turns out that the VaR figures calculated with the assumption of normal distribution significantly understate the VaR figures computed from the actual historical distribution at high confidence levels. On the other hand, for the same confidence levels, the VaR figures calculated with the assumption of t-distribution are very close to the results found using the actual historical distribution. Finally, in order to speed up the MC simulation technique, which is not commonly preferred in financial applications due to its time consuming algorithm, we implement a parallel version of it.Publisher's Versio