Conference
Learning and Intelligent Optimization
About: Learning and Intelligent Optimization is an academic conference. The conference publishes majorly in the area(s): Computer science & Optimization problem. Over the lifetime, 501 publications have been published by the conference receiving 7818 citations.
Topics: Computer science, Optimization problem, Metaheuristic, Local search (optimization), Heuristics
Papers
17 Jan 2011
TL;DR: This paper extends the explicit regression models paradigm for the first time to general algorithm configuration problems, allowing many categorical parameters and optimization for sets of instances, and yields state-of-the-art performance.
Abstract: State-of-the-art algorithms for hard computational problems often expose many parameters that can be modified to improve empirical performance. However, manually exploring the resulting combinatorial space of parameter settings is tedious and tends to lead to unsatisfactory outcomes. Recently, automated approaches for solving this algorithm configuration problem have led to substantial improvements in the state of the art for solving various problems. One promising approach constructs explicit regression models to describe the dependence of target algorithm performance on parameter settings; however, this approach has so far been limited to the optimization of few numerical algorithm parameters on single instances. In this paper, we extend this paradigm for the first time to general algorithm configuration problems, allowing many categorical parameters and optimization for sets of instances. We experimentally validate our new algorithm configuration procedure by optimizing a local search and a tree search solver for the propositional satisfiability problem (SAT), as well as the commercial mixed integer programming (MIP) solver CPLEX. In these experiments, our procedure yielded state-of-the-art performance, and in many cases outperformed the previous best configuration approach.
3,074 citations
17 Jan 2011
TL;DR: It is guess that the double-progressive widening trick can be used for other algorithms as well, as a general tool for ensuring a good bias/variance compromise in search algorithms.
Abstract: Upper Confidence Trees are a very efficient tool for solving Markov Decision Processes; originating in difficult games like the game of Go, it is in particular surprisingly efficient in high dimensional problems. It is known that it can be adapted to continuous domains in some cases (in particular continuous action spaces). We here present an extension of Upper Confidence Trees to continuous stochastic problems. We (i) show a deceptive problem on which the classical Upper Confidence Tree approach does not work, even with arbitrarily large computational power and with progressive widening (ii) propose an improvement, termed double-progressive widening, which takes care of the compromise between variance (we want infinitely many simulations for each action/state) and bias (we want sufficiently many nodes to avoid a bias by the first nodes) and which extends the classical progressive widening (iii) discuss its consistency and show experimentally that it performs well on the deceptive problem and on experimental benchmarks. We guess that the double-progressive widening trick can be used for other algorithms as well, as a general tool for ensuring a good bias/variance compromise in search algorithms.
222 citations
7 Jan 2013
TL;DR: A new way of computing Multi-points Expected Improvement criterion, without using Monte-Carlo simulations, through a closed-form formula, which allows a very fast computation of EI for reasonably low values of $$q$$ typically, less than 10.
Abstract: The Multi-points Expected Improvement criterion or $$q$$ -EI has recently been studied in batch-sequential Bayesian Optimization. This paper deals with a new way of computing $$q$$ -EI, without using Monte-Carlo simulations, through a closed-form formula. The latter allows a very fast computation of $$q$$ -EI for reasonably low values of $$q$$ typically, less than 10. New parallel kriging-based optimization strategies, tested on different toy examples, show promising results.
198 citations
10 Jun 2018
TL;DR: This study presents a new technique based on Deep Learning with Recurrent Neural Networks to address the prediction of car park occupancy rate, consisting in automatically design a deep network that encapsulates the behavior of the car occupancy and then makes an informed guess on the number of free parking spaces near to the medium time horizon.
Abstract: This study presents a new technique based on Deep Learning with Recurrent Neural Networks to address the prediction of car park occupancy rate. This is an interesting problem in smart mobility and we here approach it in an innovative way, consisting in automatically design a deep network that encapsulates the behavior of the car occupancy and then is able to make an informed guess on the number of free parking spaces near to the medium time horizon. We analyze a real world case study consisting of the occupancy values of 29 car parks in Birmingham, UK, during eleven weeks and compare our results to other predictors in the state-of-the-art. The results show that our approach is accurate to the point of being useful for being used by citizens in their daily lives, as well as it outperforms the existing competitors.
127 citations
7 Jan 2013
TL;DR: An indicator-based evolutionary multiobjective optimization algorithm EMOA is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion and first experiments indicate that the R1-EMOA accurately approximates the Pareto front of the considered continuous multiObjective optimization problems.
Abstract: An indicator-based evolutionary multiobjective optimization algorithm EMOA is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion. First experiments indicate that the R2-EMOA accurately approximates the Pareto front of the considered continuous multiobjective optimization problems. Furthermore, decision makers' preferences can be included by adjusting the weight vector distributions of the indicator which results in a focused search behavior.
117 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2022 | 39 |
| 2021 | 1 |
| 2020 | 37 |
| 2019 | 32 |
| 2018 | 40 |
| 2017 | 35 |