What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

What Can We Learn from 21 Years of School Poisonings in New Zealand?

Background: Childhood poisoning is a significant international health concern. Very little is known about trends in exposures within schools and preschools. The objectives of this study were to investigate the data recorded by the New Zealand National Poisons Centre (NPC) on these types of exposures over a 21 year period (1989 to 2009) and to determine trends and propose strategies to reduce th...

Meta-Reasoning: What Can We Learn from Meta-Memory?

Hybrid Evolutionary Multi-Objective Optimization Algorithms

This paper examines how the search ability of evolutionary multi-objective optimization (EMO) algorithms can be improved by the hybridization with local search through computational experiments on multi-objective permutation flowshop scheduling problems. The task of EMO algorithms is to find a variety of nondominated solutions of multi-objective optimization problems. First we describe our mult...

Multi-objective Optimization using Evolutionary Algorithms

This is a progress report describing my research during the last one and a half year, performed during part A of my Ph.D. study. The research field is multi-objective optimization using evolutionary algorithms, and the reseach has taken place in a collaboration with Aarhus Univerity, Grundfos and the Alexandra Institute. My research so far has been focused on two main areas, i) multi-objective ...

What we can learn from atmospheric neutrinos

Physics potential of future measurements of atmospheric neutrinos is explored. Observation of ∆m221 driven sub-dominant effects and θ13 driven large matter effects in atmospheric neutrinos can be used to study the deviation of θ23 from maximality and its octant. Neutrino mass hierarchy can be determined extremely well due to the large matter effects. New physics can be constrained both in stand...