Search results for "memetic"

Diversity Management in Memetic Algorithms

2012

In Evolutionary Computing, Swarm Intelligence, and more generally, populationbased algorithms diversity plays a crucial role in the success of the optimization. Diversity is a property of a group of individuals which indicates how much these individuals are alike. Clearly, a group composed of individuals similar to each other is said to have a low diversity whilst a group of individuals dissimilar to each other is said to have a high diversity. In computer science, in the context of population-based algorithms the concept of diversity is more specific: the diversity of a population is a measure of the number of different solutions present, see [239].

A Primer on Memetic Algorithms

2012

Memetic Algorithms (MAs) are population-based metaheuristics composed of an evolutionary framework and a set of local search algorithms which are activated within the generation cycle of the external framework, see [376]. The earliest MA implementation has been given in [621] in the context of the Travelling Salesman Problem (TSP) while an early systematic definition has been presented in [615]. The concept of meme is borrowed from philosophy and is intended as the unit of cultural transmission. In other words, complex ideas can be decomposed into memes which propagate andmutate within a population.Culture, in this way, constantly undergoes evolution and tends towards progressive improvemen…

Memory-saving optimization algorithms for systems with limited hardware

2011

Algorithmic issues in computational intelligence optimization: from design to implementation, from implementation to design

2016

The vertiginous technological growth of the last decades has generated a variety of powerful and complex systems. By embedding within modern hardware devices sophisticated software, they allow the solution of complicated tasks. As side effect, the availability of these heterogeneous technologies results into new difficult optimization problems to be faced by researchers in the field. In order to overcome the most common algorithmic issues, occurring in such a variety of possible scenarios, this research has gone through cherry-picked case-studies. A first research study moved from implementation to design considerations. Implementation limitations, such as memory constraints and real-time r…

Multilayer perceptron training with multiobjective memetic optimization

2016

Machine learning tasks usually come with several mutually conflicting objectives. One example is the simplicity of the learning device contrasted with the accuracy of its performance after learning. Another common example is the trade-off that must often be made between the rate of false positive and false negative predictions in diagnostic applications. For computer programs that learn from data, these objectives are formulated as mathematical functions, each of which describes one facet of the desired learning outcome. Even functions that intend to optimize the same facet may behave in a subtly different and mutually conflicting way, depending on the task and the dataset being examined. Mul…

Simple memetic computing structures for global optimization

2014

Meme marketing: How marketers can drive better engagement using viral memes?

2022

Scholars and industry stakeholders have exhibited an interest in identifying the underlying dimensions of viral memes. However, the recipe for creating a viral meme remains obscure. This study makes a phenomenological contribution by examining viral memes, exploring the antecedents (i.e., content-related factors, customer-related factors, and media-related factors), consequences, and moderating factors using a mixed-method approach. The study presents a holistic framework for creating viral memes based on the perceptions of customers and industry stakeholders. Four quantitative studies (i.e., a lab experiment, an online quasi-experiment, an event study, and a brand recall study) validate th…