In this paper, we propose a new multi-objective evolutionary algorithm (MOEA), which transforms a multi-objective optimization problem into a linear assignment problem using a set of weight vectors uniformly scattered. Our approach adopts uniform design to obtain the set of weights and Kuhn-Munkres’ (Hungarian) algorithm to solve the assignment problem. Differential evolution is used as our sea...

A self-adaptive Pareto Evolutionary Multiobjective Optimization (EMO) algorithm based on differential evolution is proposed for evolving locomotion controllers in an artificially embodied legged creature. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incu...

In last decades, lots of nature-inspired optimization algorithms are developed and presented to the literature for solving optimization problems. Generally, these optimization algorithms can be grouped into two categories: evolutionary algorithms and swarm intelligence methods. Evolutionary methods try to improve the candidate solutions (chromosomes) using evolutionary operators such as crossov...

In this paper we describe the Playfair substitution cipher and we propose an evolutionary algorithm for Playfair’s cryptanalysis. The structural properties of the cipher and its enciphering rules determine the suitability of an evolutionary, genetic-like approach for the cipher’s cryptanalysis. Classical cryptographic ciphers like Playfair are no longer used in practice, but evolutionary approa...

We study a coarse grained parallelization scheme (thread based) aimed at solving complex multi-objective problems by means of decomposition. Our scheme is loosely based on the MOEA/D framework. The resulting algorithm, called Parallel Decomposition (PaDe), makes use of the asynchronous generalized island model to solve the various decomposed problems. Efficient exchange of chromosomic material ...

This paper presents a new approach that combines two well-known approximations in order to improve the approximation accuracy of the Gaussian Q-function. Differential evolution, which can be considered as a simple and efficient evolutionary algorithm for optimizing real-valued optimization problems, is applied to estimate the parameters of the new approximation. The results produced by the pres...

We introduce differential fitness transmission as a signature of adaptive, Darwinian evolution, which can be detected using genealogical records of a reproducing population. This method is motivated by the observation that Darwinian evolution fundamentally consists in the differential transmission of heritable, fitness-affecting traits that result in differential transmission of fitness itself:...

The popularity of metaheuristics, especially Swarm Intelligence and Evolutionary Algorithms, has increased rapidly over the last two decades. Numerous algorithms are proposed each year, and progressively more novel applications are being found. However, different metaheuristics are often compared by their performance on problems with an arbitrarily fixed number of allowed function calls. There ...

Prototype generation techniques have arisen as very competitive methods for enhancing the nearest neighbor classifier through data reduction. Within the prototype generation methodology, the methods of adjusting the prototypes’ positioning have shown an outstanding performance. Evolutionary algorithms have been used to optimize the positioning of the prototypes with promising results. prototype...

In practice, suppliers often provide retailers with forward financing to increase demand or decrease inventory. This paper proposes a new and practical joint replenishment and delivery (JRD) model by considering trade credit. However, because of the complex mathematical properties of JRD, high-quality solutions to the problem have eluded researchers. We design an effective hybrid differential e...