Evolutionary multiobjective optimization

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He is an active member of the Evolutionary Computation research community and has been involved in organizing many of the workshops and conferences in this area.

Multi-objective optimization - Introduction

The goal of the proposed tutorial is clearly explaining difficulties of evolutionary many-objective optimization, approaches to the handling of those difficulties, and promising future research directions. Evolutionary multi-objective optimization EMO has been a very active research area in the field of evolutionary computation in the last two decades.

In the EMO area, the hottest research topic is evolutionary many-objective optimization. The difference between multi-objective and many-objective optimization is simply the number of objectives.

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Multi-objective problems with four or more objectives are usually referred to as many-objective problems. It sounds that there exists no significant difference between three-objective and four-objective problems. However, the increase in the number of objectives significantly makes multi-objective problem difficult. In the first part Part I: Difficulties , we clearly explain not only frequently-discussed well-known difficulties such as the weakening selection pressure towards the Pareto front and the exponential increase in the number of solutions for approximating the entire Pareto front but also other hidden difficulties such as the deterioration of the usefulness of crossover and the difficulty of performance evaluation of solution sets.

The attendees of the tutorial will learn why many-objective optimization is difficult for EMO algorithms. After the clear explanations about the difficulties of many-objective optimization, we explain in the second part Part II: Approaches and Future Directions how to handle each difficulty.

For example, we explain how to prevent the Pareto dominance relation from weakening its selection pressure and how to prevent a binary crossover operator from decreasing its search efficiently. We also explain some state-of-the-art many-objective algorithms. The attendees of the tutorial will learn some representative approaches to many-objective optimization and state-of-the-art many-objective algorithms. At the same time, the attendees will also learn that there still exist a large number of promising, interesting and important research directions in evolutionary many-objective optimization.

Some promising research directions are explained in detail in the tutorial.


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Many optimization problems are multiobjective in the sense that multiple, conflicting criteria need to be considered simultaneously. Due to the conflict between these objectives, usually, no single optimum solution exist. In practice, Evolutionary Multiobjective Optimization EMO algorithms are widely used for solving these multiobjective optimization problems. Being stochastic blackbox optimizers, EMO approaches allow problems with nonlinear, nondifferentiable, or noisy objective functions to be tackled. By inherently working on sets of solutions, they allow the Pareto-optimal set to be computed or approximated in one algorithm runopposed to classical techniques from the multicriteria decision making MCDM field, which aim for single solutions.

Defining problems in a multiobjective way, has two further advantages:. Within this tutorial, a comprehensive introduction to the EMO field will be given and selected research results will be presented in more detail. More specifically, we are going to i introduce the basic principles of EMO algorithms in comparison to classical solution-based approaches, ii show a few practical examples which motivate the use of EMO in terms of the mentioned innovization and multiobjectivization principles, and iii present a general overview of state-of-the-art algorithms and techniques.

Moreover, we will present important recent research results in areas such as preference articulation, performance assessment, and benchmarking. Though classified as introductory, this tutorial is intended for both novices and people familiar with EMO. Those without experience will learn about the foundations of multiobjective optimization and the basic working principles of state-of-the-art EMO algorithms.

Afterwards, he held two postdoctoral research positions in France at Inria Saclay Ile-de-France and at Ecole Polytechnique before joining Inria in November as a permanent researcher first in its Lille - Nord Europe research center and since October in the Saclay - Ile-de-France center.

His research interests are focused on evolutionary multiobjective optimization EMO , in particular on theoretical aspects of indicator-based search and on the benchmarking of blackbox algorithms in general. In the same way as evolution shapes animals, is it possible to use artificial evolution to automatically design robots?


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This attractive vision gave birth to Evolutionary Robotics ER , an interdisciplinary field that incorporates ideas from Biology, Robotics and Artificial Intelligence. This tutorial will give an overview of the various questions addressed in ER, relying on examples from the literature. Past achievements and major contributions, as well as specific challenges in ER will be described.

The tutorial will in particular focus on:. His field of research is mostly concerned with Evolutionary Computation and Complex Systems self-adaptive collective robotic systems, generative and developmental systems, evolutionary robotics. Nicolas Bredeche is author of more than 30 papers in journals and major conferences in the field.

He is also a member of the french Evolution Artificielle association and has been regularly co-organizing the french Artificial Evolution one-day seminar JET since He has also organized several international workshops in Evolution, Robotics, and Development of Artificial Neural Networks. His research is mainly concerned with the use of evolutionary algorithms in the context of optimization or synthesis of robot controllers.

He worked in a robotics context to design, for instance, controllers for flying robots, but also in the context of modelling where he worked on the use of multi-objective evolutionary algorithms to optimize and study computational models. More recently, he focused on the use of multi-objective approaches to tackle learning problems like premature convergence or generalization. Researchers of the team work on different aspects of learning in the context of motion control and cognition, both from a computational neuroscience perspective and a robotics perspective.

Jean-Baptiste Mouret is a senior researcher "Directeur de recherche" at Inria, the French research institute dedicated to computer science and mathematics.

Technical Talk: Recent Results and Open Problems in Evolutionary Multiobjective Optimization

Overall, J. Mouret conducts researches that intertwine evolutionary algorithms, neuro-evolution, and machine learning to make robots more adaptive. His work was recently featured on the cover of Nature Cully et al. The automatic design of algorithms has been an early aim of both machine learning and AI, but has proved elusive. The aim of this tutorial is to introduce hyper-heuristics as a principled approach to the automatic design of algorithms. Hyper-heuristics are metaheuristics applied to a space of algorithms; i.

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In particular, this tutorial will demonstrate how to mine existing algorithms to obtain algorithmic primitives for the hyper-heuristic to compose new algorithmic solutions from, and to employ various types of genetic programming to execute the composition process; i. This tutorial will place hyper-heuristics in the context of genetic programming - which differs in that it constructs solutions from scratch using atomic primitives - as well as genetic improvement - which takes a program as starting point and improves on it a recent direction introduced by William Langdon.

The approach proceeds from the observation that it is possible to define an invariant framework for the core of any class of algorithms often by examining existing human-written algorithms for inspiration. The variant components of the algorithm can then be generated by genetic programming. Each instance of the framework therefore defines a family of algorithms.

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While this allows searches in constrained search spaces based on problem knowledge, it does not in any way limit the generality of this approach, as the template can be chosen to be any executable program and the primitive set can be selected to be Turing-complete. Typically, however, the initial algorithmic primitive set is composed of primitive components of existing high-performing algorithms for the problems being targeted; this more targeted approach very significantly reduces the initial search space, resulting in a practical approach rather than a mere theoretical curiosity.

Iterative refining of the primitives allows for gradual and directed enlarging of the search space until convergence. This leads to a technique for mass-producing algorithms that can be customised to the context of end-use. This is perhaps best illustrated as follows: typically a researcher might create a travelling salesperson algorithm TSP by hand. When executed, this algorithm returns a solution to a specific instance of the TSP.

We will describe a method that generates TSP algorithms that are tuned to representative instances of interest to the end-user. This tutorial will provide a step-by-step guide which takes the novice through the distinct stages of automatic design. Examples will illustrate and reinforce the issues of practical application.

This technique has repeatedly produced results which outperform their manually designed counterparts, and a theoretical underpinning will be given to demonstrate why this is the case. Automatic design will become an increasingly attractive proposition as the cost of human design will only increase in-line with inflation, while the speed of processors increases in-line with Moore's law, thus making automatic design attractive for industrial application. Basic knowledge of genetic programming will be assumed.

Daniel R. He received his Ph. For several years he has served on the GECCO GA track program committee, the Congress on Evolutionary Computation program committee, and a variety of other international conference program committees. His research interests include the design of hyper-heuristics and self-configuring evolutionary algorithms and the application of computational intelligence techniques in cyber security, critical infrastructure protection, and program understanding.

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Evolutionary multiobjective optimization using a cultural algorithm - IEEE Conference Publication

He was granted a US patent for an artificially intelligent rule-based system to assist teams in becoming more effective by improving the communication process between team members. John R. Genetic programming emerged in the early 's as one of the most exciting new evolutionary algorithm paradigms. It has rapidly grown into a thriving area of research and application. While sharing the evolutionary inspired algorithm principles of a genetic algorithm, it differs by exploiting an executable genome. Genetic programming evolves a 'program' to solve a problem rather than a single solution.

This tutorial introduces the basic genetic programming paradigm. It explains how the powerful capability of genetic programming is derived from modular algorithmic components: executable representations such as a parse tree, variation operators that preserve syntax and explore a variable length, hierarchical solution space, appropriately chosen programming functions and fitness function specification. It provides demos and walks through an example of GP software.

ALFA focuses on scalable machine learning, evolutionary algorithms, and frameworks for large scale knowledge mining, prediction and analytics.

Technical talk: Recent Results and Open Problems in Evolutionary Multiobje...

The group has projects in clinical medicine knowledge discovery: arterial blood pressure forecasting and pattern recognition, diuretics in the ICU; wind energy: turbine layout optimization, resource prediction, cable layout; and MOOC Technology: MoocDB, student persistence and resource usage analysis. Her research is in the design of scalable Artificial Intelligence systems that execute on a range of hardware systems: GPUs, workstations, grids, clusters, clouds and volunteer compute networks.

These systems include machine learning components such as evolutionary algorithms e. Una-May has a patent for a original genetic algorithm technique applicable to internet-based name suggestions.