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Riccardo Poli (born 1961) is a Professor in the Department of Computing and Electronic Systems of the University of Essex. [1] His work has centered on genetic programming.
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Riccardo Poli (born 1961) is a Professor in the Department of Computing and Electronic Systems of the University of Essex. [1] His work has centered on genetic programming.
Poli started his academic career with a Laurea in electronic engineering from the University of Florence in 1989. He then did a PhD in biomedical image analysis (1993) at the same university. He later became an expert in the field of evolutionary computation, working as a Lecturer and then a Reader at the University of Birmingham from 1994 until 2001, when he moved to Essex as a professor. Poli has published around 240 refereed papers and two books (Langdon and Poli, 2002; Poli, Langdon, McPhee, 2008) on the theory and applications of genetic programming, evolutionary algorithms, particle swarm optimisation, biomedical engineering, brain–computer interfaces, neural networks, image analysis, signal processing, biology and psychology.
He is a member of the EPSRC Peer Review College, an EU expert evaluator and a grant-proposal referee for Irish, Swiss and Italian funding bodies.
He is a Fellow of the International Society for Genetic and Evolutionary Computation [2] and a recipient of the EvoStar award for outstanding contributions to this field (2007). [3] He was an ACM SIGEVO executive board member until 2013. [4] He was co-founder and co-chair of the European Conference on GP (EuroGP) (1998–2000, 2003). He was general chair (2004), track chair (2002, 2007), business committee member (2005), and competition chair (2006) of ACM’s Genetic and Evolutionary Computation Conference, co-chair of the Foundations of Genetic Algorithms Workshop (FOGA) (2002) and technical chair of the International Workshop on Ant Colony Optimisation and Swarm Intelligence (2006).
Poli is an associate editor of Genetic Programming and Evolvable Machines, Evolutionary Computation and the International Journal of Computational Intelligence Research. He is an advisory board member of the Journal on Artificial Evolution and Applications and an editorial board member of Swarm Intelligence.
Poli co-wrote Foundations of Genetic Programming and A Field Guide to Genetic Programming. A book review in Genetic Programming and Evolvable Machines noted that the latter book was unusual because it had been published under a Creative Commons license. [5]
In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.
In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. It is most commonly applied in artificial life, general game playing and evolutionary robotics. The main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use backpropagation with a fixed topology.
Linear genetic programming (LGP) is a particular method of genetic programming wherein computer programs in a population are represented as a sequence of instructions from an imperative programming language or machine language. The adjective "linear" stems from the fact that the sequence of instructions is normally executed in a linear fashion. Like in other programs, the data flow in LGP can be modeled as a graph that will visualize the potential multiple usage of register contents and the existence of structurally noneffective code (introns) which are two main differences of this genetic representation from the more common tree-based genetic programming (TGP) variant.
The IEEE Congress on Evolutionary Computation is one of the largest conferences within evolutionary computation (EC), the other conferences of similar importance being Genetic and Evolutionary Computation Conference (GECCO), Parallel Problem Solving from Nature (PPSN) and EvoStar.
Evolutionary music is the audio counterpart to evolutionary art, whereby algorithmic music is created using an evolutionary algorithm. The process begins with a population of individuals which by some means or other produce audio, which is either initialized randomly or based on human-generated music. Then through the repeated application of computational steps analogous to biological selection, recombination and mutation the aim is for the produced audio to become more musical. Evolutionary sound synthesis is a related technique for generating sounds or synthesizer instruments. Evolutionary music is typically generated using an interactive evolutionary algorithm where the fitness function is the user or audience, as it is difficult to capture the aesthetic qualities of music computationally. However, research into automated measures of musical quality is also active. Evolutionary computation techniques have also been applied to harmonization and accompaniment tasks. The most commonly used evolutionary computation techniques are genetic algorithms and genetic programming.
A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.
In artificial immune systems, clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation. Clonal selection algorithms are most commonly applied to optimization and pattern recognition domains, some of which resemble parallel hill climbing and the genetic algorithm without the recombination operator.
The School of Computer Science and Electronic Engineering at the University of Essex is an academic department that focuses on educating and researching into Computer Science and Electronic Engineering specific matters. It was formed by the merger of two departments, notable for being amongst the first in England in their fields, the Department of Electronic Systems Engineering(1966) and the Department of Computer Science (1966).
Enrique Alba is a professor of computer science at the University of Málaga, Spain.
The Genetic and Evolutionary Computation Conference (GECCO) is the premier conference in the area of genetic and evolutionary computation. GECCO has been held every year since 1999, when it was first established as a recombination of the International Conference on Genetic Algorithms (ICGA) and the Annual Genetic Programming Conference (GP).
Professor Emma Hart, FRSE is an English computer scientist known for her work in artificial immune systems (AIS), evolutionary computation and optimisation. She is a professor of computational intelligence at Edinburgh Napier University, editor-in-chief of the Journal of Evolutionary Computation, and D. Coordinator of the Future & Emerging Technologies (FET) Proactive Initiative, Fundamentals of Collective Adaptive Systems.
EvoStar, or Evo*, is an international scientific event devoted to evolutionary computation held in Europe. Its structure has evolved over time and it currently comprises four conferences: EuroGP the annual conference on Genetic Programming, EvoApplications, the International Conference on the Applications of Evolutionary Computation, EvoCOP, European Conference on Evolutionary Computation in Combinatorial Optimisation, and EvoMUSART, the International Conference on Computational Intelligence in Music, Sound, Art and Design. According to a 2016 study EvoApplications is a Q1 conference, while EuroGP and EvoCOP are both Q2. In 2021, EuroGP, EvoApplications and EvoCOP obtained a CORE rank B.
Una-May O'Reilly is an American computer scientist and leader of the Anyscale Learning For All (ALFA) group at the MIT Computer Science and Artificial Intelligence Laboratory.
Maurice Clerc is a French mathematician.
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