CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features

In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods

MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

The Extension Of Estimation Of Distribution Algorithms (Edas) To The Multiobjective Domain Has Led To Multi-Objective Optimization Edas (Moedas). Most Moedas Have Limited Themselves To Porting Single-Objective Edas To The Multi-Objective Domain. Although Moedas Have Proved To Be A Valid Approach, The Last Point Is An Obstacle To The Achievement Of A Significant Improvement Regarding "Standard" Multi-Objective Optimization Evolutionary Algorithms. Adapting The Model-Building Algorithm Is One Way To Achieve A Substantial Advance. Most Model-Building Schemes Used So Far By Edas Employ Off-The-Shelf Machine Learning Methods. However, The Model-Building Problem Has Particular Requirements That Those Methods Do Not Meet And Even Evade. The Focus Of This Paper Is On The Model- Building Issue And How It Has Not Been Properly Understood And Addressed By Most Moedas. We Delve Down Into The Roots Of This Matter And Hypothesize About Its Causes. To Gain A Deeper Understanding Of The Subject We Propose A Novel Algorithm Intended To Overcome The Draw-Backs Of Current Moedas. This New Algorithm Is The Multi-Objective Neural Estimation Of Distribution Algorithm (Moneda). Moneda Uses A Modified Growing Neural Gas Network For Model-Building (Mb-Gng). Mb-Gng Is A Custom-Made Clustering Algorithm That Meets The Above Demands. Thanks To Its Custom-Made Model-Building Algorithm, The Preservation Of Elite Individuals And Its Individual Replacement Scheme, Moneda Is Capable Of Scalably Solving Continuous Multi-Objective Optimization Problems. It Performs Better Than Similar Algorithms In Terms Of A Set Of Quality Indicators And Computational Resource Requirements.This work has been funded in part by projects CNPq BJT 407851/2012-7, FAPERJ APQ1 211.451/2015, MINECO TEC2014-57022-C2-2-R and TEC2012-37832-C02-01

Effective and efficient estimation of distribution algorithms for permutation and scheduling problems.

Estimation of Distribution Algorithm (EDA) is a branch of evolutionary computation that learn a probabilistic model of good solutions. Probabilistic models are used to represent relationships between solution variables which may give useful, human-understandable insights into real-world problems. Also, developing an effective PM has been shown to significantly reduce function evaluations needed to reach good solutions. This is also useful for real-world problems because their representations are often complex needing more computation to arrive at good solutions. In particular, many real-world problems are naturally represented as permutations and have expensive evaluation functions. EDAs can, however, be computationally expensive when models are too complex. There has therefore been much recent work on developing suitable EDAs for permutation representation. EDAs can now produce state-of-the-art performance on some permutation benchmark problems. However, models are still complex and computationally expensive making them hard to apply to real-world problems. This study investigates some limitations of EDAs in solving permutation and scheduling problems. The focus of this thesis is on addressing redundancies in the Random Key representation, preserving diversity in EDA, simplifying the complexity attributed to the use of multiple local improvement procedures and transferring knowledge from solving a benchmark project scheduling problem to a similar real-world problem. In this thesis, we achieve state-of-the-art performance on the Permutation Flowshop Scheduling Problem benchmarks as well as significantly reducing both the computational effort required to build the probabilistic model and the number of function evaluations. We also achieve competitive results on project scheduling benchmarks. Methods adapted for solving a real-world project scheduling problem presents significant improvements

Adaptive algorithms for history matching and uncertainty quantification

Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification problems. The algorithm choice and implementation, as done through a number of control parameters, have a significant impact on effectiveness and efficiency of the algorithm and thus, the quality of results and the speed of the process. This thesis is concerned with investigation, development, and implementation of improved and adaptive algorithms for reservoir history matching and uncertainty quantification problems. A set of evolutionary algorithms are considered and applied to history matching. The shared characteristic of applied algorithms is adaptation by balancing exploration and exploitation of the search space, which can lead to improved convergence and diversity. This includes the use of estimation of distribution algorithms, which implicitly adapt their search mechanism to the characteristics of the problem. Hybridising them with genetic algorithms, multiobjective sorting algorithms, and real-coded, multi-model and multivariate Gaussian-based models can help these algorithms to adapt even more and improve their performance. Finally diversity measures are used to develop an explicit, adaptive algorithm and control the algorithm’s performance, based on the structure of the problem. Uncertainty quantification in a Bayesian framework can be carried out by resampling of the search space using Markov chain Monte-Carlo sampling algorithms. Common critiques of these are low efficiency and their need for control parameter tuning. A Metropolis-Hastings sampling algorithm with an adaptive multivariate Gaussian proposal distribution and a K-nearest neighbour approximation has been developed and applied

Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem.

Multi-Mode Resource Constrained Project Problem (MRCPSP) is a multi-component problem which combines two interacting sub-problems; activity scheduling and mode assignment. Multi-component problems have been of research interest to the evolutionary computation community as they are more complex to solve. Estimation of Distribution Algorithms (EDAs) generate solutions by sampling a probabilistic model that captures key features of good solutions. Often they can significantly improve search efficiency and solution quality. Previous research has shown that the mode assignment sub-problem can be more effectively solved with an EDA. Also, a competitive Random Key based EDA (RK-EDA) for permutation problems has recently been proposed. In this paper, activity and mode solutions are respectively generated using the RK-EDA and an integer based EDA. This approach is competitive with leading approaches of solving the MRCPSP

TEDA: A Targeted Estimation of Distribution Algorithm

This thesis discusses the development and performance of a novel evolutionary algorithm, the Targeted Estimation of Distribution Algorithm (TEDA). TEDA takes the concept of targeting, an idea that has previously been shown to be effective as part of a Genetic Algorithm (GA) called Fitness Directed Crossover (FDC), and introduces it into a novel hybrid algorithm that transitions from a GA to an Estimation of Distribution Algorithm (EDA). Targeting is a process for solving optimisation problems where there is a concept of control points, genes that can be said to be active, and where the total number of control points found within a solution is as important as where they are located. When generating a new solution an algorithm that uses targeting must first of all choose the number of control points to set in the new solution before choosing which to set. The hybrid approach is designed to take advantage of the ability of EDAs to exploit patterns within the population to effectively locate the global optimum while avoiding the tendency of EDAs to prematurely converge. This is achieved by initially using a GA to effectively explore the search space before transitioning into an EDA as the population converges on the region of the global optimum. As targeting places an extra restriction on the solutions produced by specifying their size, combining it with the hybrid approach allows TEDA to produce solutions that are of an optimal size and of a higher quality than would be found using a GA alone without risking a loss of diversity. TEDA is tested on three different problem domains. These are optimal control of cancer chemotherapy, network routing and Feature Subset Selection (FSS). Of these problems, TEDA showed consistent advantage over standard EAs in the routing problem and demonstrated that it is able to find good solutions faster than untargeted EAs and non evolutionary approaches at the FSS problem. It did not demonstrate any advantage over other approaches when applied to chemotherapy. The FSS domain demonstrated that in large and noisy problems TEDA’s targeting derived ability to reduce the size of the search space significantly increased the speed with which good solutions could be found. The routing domain demonstrated that, where the ideal number of control points is deceptive, both targeting and the exploitative capabilities of an EDA are needed, making TEDA a more effective approach than both untargeted approaches and FDC. Additionally, in none of the problems was TEDA seen to perform significantly worse than any alternative approaches

Variational Autoencoder Based Estimation Of Distribution Algorithms And Applications To Individual Based Ecosystem Modeling Using EcoSim

Individual based modeling provides a bottom up approach wherein interactions give rise to high-level phenomena in patterns equivalent to those found in nature. This method generates an immense amount of data through artificial simulation and can be made tractable by machine learning where multidimensional data is optimized and transformed. Using individual based modeling platform known as EcoSim, we modeled the abilities of elitist sexual selection and communication of fear. Data received from these experiments was reduced in dimension through use of a novel algorithm proposed by us: Variational Autoencoder based Estimation of Distribution Algorithms with Population Queue and Adaptive Variance Scaling (VAE-EDA-Q AVS). We constructed a novel Estimation of Distribution Algorithm (EDA) by extending generative models known as variational autoencoders (VAE). VAE-EDA-Q, proposed by us, smooths the data generation process using an iteratively updated queue (Q) of populations. Adaptive Variance Scaling (AVS) dynamically updates the variance at which models are sampled based on fitness. The combination of VAE-EDA-Q with AVS demonstrates high computational efficiency and requires few fitness evaluations. We extended VAE-EDA-Q AVS to act as a feature reducing wrapper method in conjunction with C4.5 Decision trees to reduce the dimensionality of data. The relationship between sexual selection, random selection, and speciation is a contested topic. Supporting evidence suggests sexual selection to drive speciation. Opposing evidence contends either a negative or absence of correlation to exist. We utilized EcoSim to model elitist and random mate selection. Our results demonstrated a significantly lower speciation rate, a significantly lower extinction rate, and a significantly higher turnover rate for sexual selection groups. Species diversification was found to display no significant difference. The relationship between communication and foraging behavior similarly features opposing hypotheses in claim of both increases and decreases of foraging behavior in response to alarm communication. Through modeling with EcoSim, we found alarm communication to decrease foraging activity in most cases, yet gradually increase foraging activity in some other cases. Furthermore, we found both outcomes resulting from alarm communication to increase fitness as compared to non-communication

Interval-based ranking in noisy evolutionary multiobjective optimization

As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present ?-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standardmulti-objective evolutionary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experimental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization