Many real-world problems show both multiobjective as well as dynamic characteristics. In order to use multiobjective evolutionary optimization algorithms (MOEA) efficiently, a systematic analysis of the behavior of these algorithms in dynamic environments is necessary. Dynamic fitness functions can be classified into problems with moving Pareto fronts and Pareto sets having varying speed, shape...

The ubiquitous polyamines fulfil a variety of functions in all three kingdoms of life. However, little is known about the biosynthesis of these compounds in Gram-positive bacteria. We show that, in Bacillus subtilis, there is a single pathway to polyamines, starting from arginine, with agmatine as an intermediate. We first identified the structural gene of arginine decarboxylase, speA (formerly...

Multicast routing problem in computer networks, with more than one objective to consider, like cost and delay, is usually treated as a mono-objective Optimization Problem, where the cost of the tree is minimized subject to a priori restrictions on the delays from the source to each destination. This paper presents a new multicast algorithm based on the Strength Pareto Evolutionary Algorithm (SP...

Escherichia coli K-12 mutants that carry deletions in their genes for ornithine decarboxylase (L-ornithine carboxy-lyase, EC 4.1.1.17) (speC), arginine decarboxylase (L-arginine carboxy-lyase, EC 4.1.1.19) (speA), and agmatine ureohydrolase (agmatinase or agmatine amidinohydrolase, EC 3.5.3.11) (speB) can still synthesize very small amounts of putrescine and spermidine. The putrescine concentra...

A new evolutionary multi-objective crowding algorithm (EMOCA) is evaluated using nine benchmark multiobjective optimization problems, and shown to produce non-dominated solutions with significant diversity, outperforming state-of-the-art multi-objective evolutionary algorithms viz., Non-dominated Sorting Genetic Algorithm – II (NSGA-II), Strength Pareto Evolutionary algorithm II (SPEA-II) and P...

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...

In this paper we assess the performance of three modern mul-tiobjective evolutionary algorithms on a real-world optimization problem related to the management of distributed databases. The algorithms assessed are the Strength Pareto Evolutionary Algorithm (SPEA), the Pareto Archived Evolution Strategy (PAES), and M-PAES, which is a Memetic Algorithm based variant of PAES. The performance of the...

A cluster of 5 family members, a mother and 4 children, were hospitalized for severe group A Streptococcus (GAS) pneumonia. Three family members had complications: sepsis (1), empyema (2), and a sterile parapneumonic effusion (1). Two additional family members had symptoms of upper respiratory tract infection, and 1 was hospitalized for these symptoms. GAS was isolated from the blood of 1 patie...

A Genetic Algorithm (GA) is the process of constructing an optimization problem in which several objectives can be optimized at the same time. In this paper, Strength Pareto Evolutionary Algorithm (SPEA), a GA based multi-objective optimization technique, has been applied to a graph drawing (GD) problem. In this paper a measure (force equalization) which contributes to production of nicely draw...