# Carlos CottaUniversity of Malaga | UMA · Department of Computer Sciences and Languages

Carlos Cotta

PhD Computer Science

## About

336

Publications

93,627

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Introduction

Carlos Cotta currently works at the Department of Computer Sciences and Languages, University of Malaga. Carlos does research in Artificial Intelligence, Evolutionary Algorithms, Distributed Environments and Complex Systems. His current project is 'Deep Bioinspired Algorithms in Massively Complex Enviroments (DeepBio).'

Additional affiliations

November 2017 - present

November 2001 - November 2017

## Publications

Publications (336)

Memetic Algorithms and, in general, approaches underneath the wider Memetic Computing paradigm, have been at the core of a frantic research activity since the very inception of this research area in the late eighties. The community working in this area has so far showcased the benefits of hybridizing population-based algorithms with trajectory-base...

The template design problem (TDP) is a hard combinatorial problem with a high number of symmetries which makes solving it more complicated. A number of techniques have been proposed in the literature to optimise its resolution, ranging from complete methods to stochastic ones. However, although metaheuristics are considered efficient methods that c...

This book constitutes the refereed proceedings of the 19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020, which was cancelled due to the COVID-19 pandemic, amalgamated with CAEPIA 2021, and held in Malaga, Spain, during September 2021.
The 25 full papers presented were carefully selected from 40 submissions. The Co...

Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous basic optimization algorithms that interact among them in a cooperative way in order to deal with a specifi...

The Balanced Incomplete Block Design (BIBD) problem is a difficult combinatorial problem with a large number of symmetries, which add complexity to its resolution. In this paper, we propose a dual (integer) problem representation that serves as an alternative to the classical binary formulation of the problem. We attack this problem incrementally:...

Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper proposes the use of evolutionary algorithms (EAs) to d...

We consider an operational model of suicide bombing attacks—an increasingly prevalent form of terrorism—against specific targets, and the use of protective countermeasures based on the deployment of detectors over the area under threat. These detectors have to be carefully located in order to minimize the expected number of casualties or the econom...

Meta-analytics represents the unification of metaheuristics and analytics, two fields of the foremost interest and practical importance. While metaheuristics provide a modern framework and an arsenal of cutting-edge techniques to handle complex, real-world problems, Analytics embodies the use of prediction and optimization techniques in practical c...

Each instance contains n strings (possibly of different lengths) defined over an alphabet of m symbols. Some instances have been randomly generated and some other instances have been obtained from biological sequences.

The concept of Ephemeral Computing is an emergent topic that is currently consolidating among the research community. It includes computing systems where the nodes or the connectivity have an ephemeral and thus unpredictable nature. Although the capacity and computer power of small and medium devices (as smartphones or tablets) are increasing swift...

We consider the use of island-based evolutionary algorithms (EAs) on fault-prone computational settings. More precisely, we consider scenarios plagued with correlated node failures. To this end, we use the sandpile model in order to induce such complex, correlated failures in the system. Several EA variants featuring self-adaptive capabilities aime...

Memetic algorithms (MAs) are optimization techniques based on the orchestrated interplay between global and local search components and have the exploitation of specific problem knowledge as one of their guiding principles. In its most classical form, a MA is typically composed of an underlying population-based engine onto which a local search comp...

Computational environments emerging from the pervasiveness of networked devices offer a plethora of opportunities and challenges. The latter arise from their dynamic, inherently volatile nature that tests the resilience of algorithms running on them. Here we consider the deployment of population-based optimization algorithms on such environments, u...

Memetic algorithms provide one of the most effective and flexible metaheuristic approaches for tackling hard optimization problems. Memetic algorithms address the difficulty of developing high-performance universal heuristics by encouraging the exploitation of multiple heuristics acting in concert, making use of all available sources of information...

Ephemeral computing is a term that describes computing systems whose nodes or their connectivity have an ephemeral, heterogeneous and possibly also unpredictable nature. These properties will affect the functioning of distributed versions of computer algorithms. Such algorithms, which are usually straightforward extensions of sequential algorithms,...

Suicide bombing is an infamous form of terrorism that is becoming increasingly prevalent in the current era of global terror warfare. We consider the case of targeted attacks of this kind, and the use of detectors distributed over the area under threat as a protective countermeasure. Such detectors are non-fully reliable, and must be strategically...

We consider the deployment of island-based evolutionary algorithms (EAs) on unstable networks whose nodes exhibit correlated failures. We use the sandpile model in order to induce such complex, correlated failures in the system. A performance analysis is conducted, comparing the results obtained in both correlated and non-correlated scenarios for i...

We consider the deployment of island-based evolutionary algorithms (EAs) on irregular computational environments plagued with different kind of glitches. In particular we consider the effect that factors such as network latency and transient process suspensions have on the performance of the algorithm. To this end, we have conducted an extensive ex...

Bioinspired algorithms are search, optimization, and learning techniques whose functioning is based on some metaphor of a biological process. Prominent examples include evolutionary algorithms and swarm intelligence methods. The practical application of these techniques to real-world problems typically involves orchestrating the interplay among dif...

Software development teams eventually become complex systems reaching a critical state, a fact that has already been proved by several researchers. This state, reached by self-organization, is characterized by three conditions applied to the sequence of changes: a scale-free structure, long-distance correlations, and so-called pink noise. In this p...

The performance of island-based evolutionary algorithms is studied on unstable networks whose nodes exhibit complex correlated failures. Simple EAs have a significant performance degradation with respect to networks with uncorrelated failures, but the use of self-⋆ properties allows the EA to increase its resilience in this scenario.

Optimization algorithms deployed on unstable computational environments must be resilient to the volatility of computing nodes. Different fault-tolerance mechanisms have been proposed for this purpose. We focus on the use of island-based multimemetic algorithms, namely memetic algorithms which explicitly represent and evolve memes alongside solutio...

Memetic algorithms (MAs) constitute a search and optimization paradigm based on the orchestrated interplay between global and local search components, and have the exploitation of specific problem knowledge as one of their central tenets. MAs can take different forms although a classical incarnation involves the integration of independent search pr...

This paper investigates a Particle Swarm with dynamic topology and a conservation of evaluations strategy. The population is structured on a 2-dimensional grid of nodes, through which the particles interact and move according to simple rules. As a result of this structure, each particle’s neighbourhood degree is time-varying. If at given time step...

Energy consumption is a matter of paramount importance in nowadays environmentally conscious society. It is also bound to be a crucial issue in light of the emergent computational environments arising from the pervasive use of networked handheld devices and wearables. Evolutionary algorithms (EAs) are ideally suited for this kind of environments du...

We consider the deployment of island-based memetic algorithms (MAs) endowed with \(\text {self-}{\star }\) properties on unstable computational environments composed of a collection of computing nodes whose availability fluctuates. In this context, these properties refer to the ability of the MA to work autonomously in order to optimize its perform...

Complexity is a prevalent feature of numerous natural and artificial systems and as such has attracted much scientific interest in the last decades. The pursuit of computational tools capable of analyzing, modeling or designing systems exhibiting this complex nature –in which the properties of the system are not evident at the bottom level but emer...

Multimemetic algorithms (MMAs) are memetic algorithms that explicitly exploit the evolution of memes, i.e., non-genetic expressions of problem-solving strategies. We aim to study their deployment on an unstable environment with complex topology and volatile resources. We analyze their behavior and performance on environments with different churn ra...

In most computer games as in life, the outcome of a match is uncertain due to several reasons: the characters or assets appear in different initial positions or the response of the player, even if programmed, is not deterministic; different matches will yield different scores. That is a problem when optimizing a game-playing engine: its fitness wil...

In many optimization processes, the fitness or the considered measure of goodness for the candidate solutions presents uncertainty, that is, it yields different values when repeatedly measured, due to the nature of the evaluation process or the solution itself. This happens quite often in the context of computational intelligence in games, when eit...

It is increasingly common that computational devices with significant computing power are underexploited. Some of the reasons for that are due to frequent idle-time or to the low computational demand of the tasks they perform, either sporadically or in their regular duty. The exploitation of this (otherwise-wasted) computational power is a cost-eff...

The Particle Swarm Optimization (PSO) algorithm is a population-based metaheuristics in which the individuals communicate through decentralized networks. The network can be of many forms but traditionally its structure is predetermined and remains fixed during the search. This paper investigates an alternative approach. The particles are positioned...

Memetic algorithms provide one of the most effective and flexible metaheuristic approaches for tackling hard optimization problems. Memetic algorithms address the difficulty of developing high-performance universal heuristics by encouraging the exploitation of multiple heuristics acting in concert, making use of all available sources of information...

Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with a spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under...

The use of volatile decentralized computational platforms such as, e.g., peer-to-peer networks, is becoming an increasingly popular option to gain access to vast computing resources. Making an effective use of these resources requires algorithms adapted to such a changing environment, being resilient to resource volatility. We consider the use of a...

Optimization algorithms deployed on unstable computational environments must be resilient to the volatility of computing nodes. Different fault-tolerance mechanisms have been proposed for this purpose. We focus on the use of dynamic population sizes in the context of island-based multimemetic algorithms, namely memetic algorithms which explicitly r...

The Far From Most String Problem (FFMSP) is a string selection problem. The objective is to find a string whose distance to other strings in a certain input set is above a given threshold for as many of those strings as possible. This problem has links with some tasks in computational biology and its resolution has been shown to be very hard. We pr...

We study the behavior and performance of island-based multimemetic algorithms, namely memetic algorithms which explicitly represent and evolve memes alongside solutions, in unstable computational environments whose topology is modeled as scale-free networks, a pattern of connectivity observed in real-world networks, such as peer-to-peer systems. We...

Videogames are one of the most important and profitable sectors in the industry of entertainment. Nowadays, the creation of a videogame is often a large-scale endeavor and bears many similarities with, e.g., movie production. On the central tasks in the development of a videogame is content generation, namely the definition of maps, terrains, non-p...

The use of parallel and distributed models of evolutionary algorithms (EAs) is widespread nowadays as a means to improve solution quality and reduce computational times when solving hard optimization problems. For this purpose, emergent computational environments such as P2P networks and desktop grids are offering a plethora of new opportunities bu...

This paper discusses some of the most interesting challenges to which the games research community members may face in the area of the application of artificial or computational intelligence techniques to the design and creation of video games. The paper focuses on three lines that certainly will influence significantly the industry of game develop...

The classical approach of Competitive Coevolution (CC) applied in games tries to exploit an arms race between coevolving populations that belong to the same species (or at least to the same biotic niche), namely strategies, rules, tracks for racing, or any other. This paper proposes the co-evolution of entities belonging to different realms (namely...

This chapter presents an overview of hybridization mechanisms in evolutionary algorithms. Such mechanisms are aimed to introducing problem knowledge in the optimization technique by means of the synergistic combination of general-purpose methods and problemspecific add-ons. This combination is presented in this work from two wide perspectives: meme...

Computational devices with significant computing power are pervasive yet often under-exploited since they
are frequently idle or performing non-demanding tasks. Exploiting this power can be a cost-effective solution
for solving complex computational tasks. Device-wise, this computational power can some times comprise a
stable, long-lasting availabi...

The use of parallel and distributed models of metaheuristics has steadily settled in the last decades into an accessible mechanism for improving both the efficiency and the effectiveness of the search process.While population-based techniques can be readily deployed on dedicated distributed systems, their use on unstable computational environments...

Procedural content generation (PCG) is a research field on the rise, with numerous papers devoted to this topic. This paper presents a PCG method based on a self-adaptive evolution strategy for the automatic generation of maps for the real-time strategy (RTS) game Planet Wars. These maps are generated in order to fulfill the aesthetic preferences o...

Multimemetic algorithms (MMAs) are memetic algorithms in which memes (interpreted as non-genetic expressions of problem-solving strategies) are explicitly represented and evolved alongside genotypes. This process is commonly approached using the standard genetic procedures of recombination and mutation to manipulate directly information at the meme...

Multimemetic algorithms (MMAs) are memetic algorithms that explicitly represent and evolve memes (computational representations of problem solving methods) as a part of solutions. We consider an island-based model of MMAs and provide a comparative analysis of six migrant selection strategies and two migrant replacement operators. We use a test suit...

This paper studies a self-organized framework for modeling dynamic topologies in spatially structured Evolutionary Algorithms (EAs). The model consists of a 2-dimensional grid of nodes where the individuals interact and self-organize into clusters. During the search process, the individuals move through the grid, following a pre-defined simple rule...

This paper presents a procedural content generation (PCG) method that is able to generate aesthetic maps for a real-time strategy game. The maps were characterized based on either their geometrical properties or their topological measures (obtained in this latter case from the sphere-of-influence graph induced by each map). Using these features, a...

Multimemetic algorithms (MMAs) are memetic algorithms that explicitly exploit the evolution of memes, i.e., non-genetic expressions of problem-solving strategies. We consider a class of MMAs in which these memes are rewriting rules whose length can be fixed during the run of the algorithm or self-adapt during the search process. We analyze this sel...

This work presents a procedural content generation system that uses an evolutionary algorithm in order to generate interesting maps for a real-time strategy game, called Planet Wars. Interestingness is here captured by the dynamism of games (i.e., the extent to which they are action-packed). We consider two different approaches to measure the dynam...

We consider search-based procedural content generation in the context of Planet Wars, an RTS game. The objective of this work is to generate maps for the aforementioned game, that result in an interesting game-play. In order to characterize interestingness we focus on the properties of balance and dynamism. The former captures the fact that no play...

The Google Artificial Intelligence (AI) Challenge is an international contest the objective of which is to program the AI in a two-player real time strategy (RTS) game. This AI is an autonomous computer program that governs the actions that one of the two players executes during the game according to the state of play. The entries are evaluated via...

This paper investigates dynamic and partially connected ring topologies for cellular Evolutionary Algorithms (cEA). We hypothesize that these structures maintain population diversity at a higher level and reduce the risk of premature convergence to local optima on deceptive, multimodal and NP-hard fitness landscapes. A general framework for modelli...