Efficient graph-based genetic programming representation with multiple outputs

In this work, we explore and study the implication of having more than one output on a genetic programming (GP) graph-representation. This approach, called multiple interactive outputs in a single tree (MIOST), is based on two ideas. First, we defined an approach, called interactivity within an individual (IWI), which is based on a graph-GP representation. Second, we add to the individuals created with the IWI approach multiple outputs in their structures and as a result of this, we have MIOST. As a first step, we analyze the effects of IWI by using only mutations and analyze its implications (i.e., presence of neutrality). Then, we continue testing the effectiveness of IWI by allowing mutations and the standard GP crossover in the evolutionary process. Finally, we tested the effectiveness of MIOST by using mutations and crossover and conducted extensive empirical results on different evolvable problems of different complexity taken from the literature. The results reported in this paper indicate that the proposed approach has a better overall performance in terms of consistency reaching feasible solutions

Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference

CAPACIDADES TÉCNICAS, LEGALES Y DE GESTIÓN PARA EQUIPOS DE BLUE TEAM Y RED TEAM

El presente documento técnico relaciona los procesos, métodos, herramientas y marcos legales que rigen a los equipos rojos y azules en cualquier organización con una infraestructura de TI. A través de la conceptualización y la práctica virtual, en este documento se aplica la estrategia de PBL (Problem-Based Learning) en tres fases, en las cuales se presenta en la primera fase los temas jurídicos y cuestiones éticas. Estos son los marcos que todos los miembros de un equipo rojo o azul deben implementar para realizar sus tareas diarias de ciberseguridad, para evaluar el alcance que cualquier integrante de estos equipos debe tener en cuenta. La fase dos está estructuradas con un conjunto de estrategias y herramientas en pro de apoyar la implementación y cumplimiento de los procesos de detección de fallos de seguridad TI y plataformas TI, identificando las metodologías de pruebas de penetración a tener en cuenta para la solución de un problema presentado. Por último, en la fase tres consiste en un conjunto de procesos y herramientas encaminadas a la contención de un ataque informático y su posterior corrección, a partir del establecimiento de buenas prácticas de ciberseguridad en la organización y de esta forma proteger la información de las organizaciones. PALABRAS CLAVES: Hardenización, Proteger, Red Team y Blue Team, Riesgo, Vulnerabilidad.This technical document relates the processes, methods, tools and legal frameworks that govern the red and blue teams in any organization with an IT infrastructure. Through conceptualization and virtual practice, this document applies the PBL (Problem-Based Learning) strategy in three phases, in which legal issues and ethical issues are presented in the first phase. These are the frameworks that all members of a red or blue team must implement to carry out their daily cybersecurity tasks, to assess the scope that any member of these teams must take into account. Phase two is structured with a set of strategies and tools to support the implementation and compliance of IT security flaw detection processes and IT platforms, identifying the penetration testing methodologies to be taken into account for the solution of a problem presented. Finally, phase three consists of a set of processes and tools aimed at containing a computer attack and its subsequent correction, based on the establishment of good cybersecurity practices in the organization and thus protect the information of organizations. . KEY WORDS: Hardening, Protect, Red Team and Blue Team, Risk, Vulnerability

Patrimonio y Desarrollo Local En Clave Municipal: Santa Cruz De Tenerife Como Referencia

El municipio de Santa Cruz de Tenerife, a raíz de su asentamiento territorial, su evolución histórica y su crecimiento urbanístico, alberga entre sus lindes numerosos elementos y bienes que no se han valorado lo suficiente. Por ello, es necesario elaborar una estrategia que incluya este patrimonio de inmensas posibilidades y de incalculable valor, que debe ser cuidado y divulgado a la ciudadanía, con el fin de conseguir que sean recursos útiles para promover un proceso de desarrollo local en el municipio considerando especialmente su localización diversificada en los diferentes distritos y barrios

Behavioural responses of white sharks to specific baits during cage diving ecotourism

This study describes the effect of different baits on the attraction, surface behaviour and conditioning of white sharks Carcharodon carcharias during local ecotourism activities. The sightings, behaviours, and pictures used for photographic identification were obtained during August to November 2012-2014 onboard tourist boats in Guadalupe Island, Mexico. Four types of baits were used: (1) frozen bait; (2) frozen bait and natural chum; (3) fresh fish bait; and (4) mackerel bags. Data were analysed according to sex, maturity and the total of sharks using 6,145 sightings of 121 white sharks. The type of bait showed no significant difference on the effectiveness to attracting sharks. Ethological analysis showed that the type of bait had a significant effect on the shark's surface behaviour during its interactions with boats. Natural chum and fresh baits showed short term behavioural patterns constituted by increased number of violent interactions with the bait, while the frozen bait did not generate a defined behavioural pattern. Conditioning of white sharks was determined by the number of interactions and the consumption frequency of the bait. Fifty nine percent of sharks (n=41) showed no conditioning, 36% (n=25) showed a low risk and only 5% (n=3) were found to have a high risk of conditioning. The results suggest that current ecotourism has no effect on the conditioning of the white sharks, and that all baits have a similar effectiveness for attracting the sharks. However, a different behavioural pattern was observed when fresh bait and chum were used, which could increase the potential of accidents during ecotourism

An Empirical Investigation of How Degree Neutrality Affects GP Search

Over the last years, neutrality has inspired many researchers in the area of Evolutionary Computation (EC) systems in the hope that it can aid evolution. However, there are contradictory results on the effects of neutrality in evolutionary search. The aim of this paper is to understand how neutrality - named in this paper degree neutrality - affects GP search. For analysis purposes, we use a well-defined measure of hardness (i.e., fitness distance correlation) as an indicator of difficulty in the absence and in the presence of neutrality, we propose a novel approach to normalise distances between a pair of trees and finally, we use a problem with deceptive features where GP is well-known to have poor performance and see the effects of neutrality in GP search