Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.
problem Maximizing returns while minimizing risk in portfolio optimization.
method Incorporates pre-assignment constraints and margin trading strategies using Genetic Algorithms and Particle Swarm Optimization.
result Metaheuristic-based portfolio optimization yields superior risk-adjusted returns compared to traditional methods.
Time-limited metaheuristics find near-optimal solutions for constrained portfolio optimisation.
problem Finding near-optimal solutions for constrained portfolio optimisation within limited computation time.
method Time-limited metaheuristics (simulated annealing, tabu search, genetic algorithm) tested on historical market data.
result Simulated annealing found near-optimal solutions in 5 seconds across most datasets.
Metaheuristics improve yield curve estimation for Costa Rica.
problem Estimating the yield curve for Costa Rica using historical data.
method Used Nelson-Siegel and Svensson models with four metaheuristics (Ant colony, Genetic, Particle Swarm, Simulated Annealing) for optimization.
result Metaheuristics achieved better results than classical methods, especially Particle Swarm and Simulated Annealing.
Two binary Sine Cosine Algorithms improve feature selection in medical datasets.
problem Optimizing feature selection from medical datasets to enhance model accuracy.
method Proposed SBSCA and VBSCA algorithms using S-shaped and V-shaped transfer functions.
result SBSCA and VBSCA outperform four other binary optimization algorithms in medical datasets.
SIGMA improves IDS robustness against new attacks using GAN and metaheuristics.
problem Lack of robustness in IDS against new, unseen attacks.
method SIGMA uses GAN and metaheuristics to generate adversarial examples and retrain IDS.
result SIGMA can improve IDS performance up to 100% after two rounds of improvement.
Study proposes a new method for better price prediction using machine learning and metaheuristics.
problem Challenges in predicting prices due to correlated variables and computational efficiency.
method Introduces a novel decision fusion approach combining Elastic Net and MOPSO for variable selection and prediction.
result The proposed method outperforms traditional approaches in terms of accuracy and efficiency.
Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received …
A metaheuristic approach solves portfolio optimization with constraints.
problem Portfolio Optimization Problem with cardinality and quantity constraints.
method Combination of TabuSearch and TokenRing Search with three neighborhood relations.
result The proposed techniques perform well on public benchmarks.
MaNet uses DL for feature selection to solve optimization problems.
problem Optimization problems with millions of parameters.
method Deep learning model with feature selection for continuous optimization.
result MaNet yields competitive results compared to hand-designed algorithms.
CASP improves portfolio optimization by considering asset covariance.
problem Infeasibility in cardinality-constrained portfolio optimization.
method CASP uses volatility-normalized selection and covariance-aware projection.
result CASP-Basic delivers lower portfolio variance than standard Euclidean repair.
A new algorithm improves solving QAP with better performance.
problem Solving the Quadratic Assignment Problem (QAP) efficiently.
method Estimation of Distribution Algorithms (EDAs) with a non-parametric distance-based Mallows model.
result The proposed algorithm outperforms existing methods for QAP.
Gradient Descent (GD) approximators often fail in the solution space with multiple scales of convexities, i.e., in subspace learning and neural network scenarios. To handle that, one solution is to run GD multiple times from different randomized initial states and select the best solution over all experiments. However,…
Algorithm improves vanilla option pricing accuracy during and before COVID-19.
problem Improving vanilla option pricing accuracy during and before the pandemic.
method Combinational Mutation Strategy of Differential Evolution (CmDE) algorithm for bi-objective optimization.
result Algorithm approximates real market vanilla option prices more accurately than Black-Scholes.
We propose a new metaheuristic training scheme that combines Stochastic Gradient Descent (SGD) and Discrete Optimization in an unconventional way. Our idea is to define a discrete neighborhood of the current SGD point containing a number of "potentially good moves" that exploit gradient information, and to search this …
A new model tracks indices without rebalancing, solving NP-hard problems.
problem Tracking indices without rebalancing and minimizing deviations.
method Metaheuristic algorithms and local branching for solving mixed integer linear programming.
result The heuristic generates portfolios that outperform commercial solvers in both in-sample and out-of-sample data.
The prediction of stock prices is an important task in economics, investment and making financial decisions. This has, for decades, spurred the interest of many researchers to make focused contributions to the design of accurate stock price predictive models; of which some have been utilized to predict the next day ope…
Proposes a novel evolutionary model for stock price prediction.
problem Challenges in financial markets, such as adaptability and interpretability.
method Trader-Company method, which aggregates suggestions from multiple weak learners (Traders) to predict stock returns.
result Shows the effectiveness of the method through experiments on real market data.
Curriculum learning has been successfully used in reinforcement learning to accelerate the learning process, through knowledge transfer between tasks of increasing complexity. Critical tasks, in which suboptimal exploratory actions must be minimized, can benefit from curriculum learning, and its ability to shape explor…
This review analyzes recent advances in solving index tracking problems.
problem Creating a portfolio that closely follows a specific index with lower costs.
method Systematic review of mathematical approaches and metaheuristics.
result Metaheuristics have been extensively applied and improved in solving index tracking problems.
New clustering methods for binary data using combinatorial optimization.
problem Clustering binary data efficiently and effectively.
method Five new combinatorial optimization heuristics (SA, TA, TS, GA, ACO) applied to binary data.
result Simulated annealing performs exceptionally well compared to classical methods.
Cluster-wise linear regression (CLR), a clustering problem intertwined with regression, is to find clusters of entities such that the overall sum of squared errors from regressions performed over these clusters is minimized, where each cluster may have different variances. We generalize the CLR problem by allowing each…
We consider the optimization of active extension portfolios. For this purpose, the optimization problem is rewritten as a stochastic programming model and solved using a clever multi-start local search heuristic, which turns out to provide stable solutions. The heuristic solutions are compared to optimization results o…
AWDO trains neural networks for digit classification.
problem Training feedforward neural networks.
method Adaptive Wind Driven Optimization (AWDO).
result AWDO outperforms steepest descent method in digit classification.
In today world of enormous amounts of data, it is very important to extract useful knowledge from it. This can be accomplished by feature subset selection. Feature subset selection is a method of selecting a minimum number of features with the help of which our machine can learn and predict which class a particular dat…
Minimum sum-of-squares clustering (MSSC) is a widely used clustering model, of which the popular K-means algorithm constitutes a local minimizer. It is well known that the solutions of K-means can be arbitrarily distant from the true MSSC global optimum, and dozens of alternative heuristics have been proposed for this …
We investigate the application of two heuristic methods, genetic algorithms and tabu/scatter search, to the optimisation of realistic portfolios. The model is based on the classical mean-variance approach, but enhanced with floor and ceiling constraints, cardinality constraints and nonlinear transaction costs which inc…
Paper uses RL to solve constrained combinatorial optimization problems.
problem Constrained combinatorial optimization problems.
method Extending NCO theory to CMDPs, using RL with penalty signals.
result Proposes a superior method for solving constrained problems.
Curriculum learning in reinforcement learning is used to shape exploration by presenting the agent with increasingly complex tasks. The idea of curriculum learning has been largely applied in both animal training and pedagogy. In reinforcement learning, all previous task sequencing methods have shaped exploration with …
Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality, by either selecting a subset of features or removing unrelated ones. This paper presents …
Optimizes task allocation for financial analysts to balance work efficiency and well-being.
problem Balancing business goals with financial analysts' well-being in error resolution tasks.
method Used a Genetic Algorithm (GA) to optimize task allocation considering both business goals and analyst well-being.
result GA model outperforms existing methods and is applicable to various real-world scenarios.
We present a simple transformation of the formulation of the log-periodic power law formula of the Johansen-Ledoit-Sornette model of financial bubbles that reduces it to a function of only three nonlinear parameters. The transformation significantly decreases the complexity of the fitting procedure and improves its sta…
Evolutionary algorithms (EAs) are a sort of nature-inspired metaheuristics, which have wide applications in various practical optimization problems. In these problems, objective evaluations are usually inaccurate, because noise is almost inevitable in real world, and it is a crucial issue to weaken the negative effect …
An algorithmic limit of compressed sensing or related variable-selection problems is analytically evaluated when a design matrix is given by an overcomplete random matrix. The replica method from statistical mechanics is employed to derive the result. The analysis is conducted through evaluation of the entropy, an expo…
Recently, hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques, where frequent patterns found in high-quality solutions can lead to an efficient exploration of the search space, along with a si…
ProGO optimizes non-convex functions without gradients, outperforming existing methods.
problem Challenges in global optimization, especially with non-convex functions and limited gradient information.
method Probabilistic approach using multidimensional integration and latent slice sampler.
result ProGO converges to global optima efficiently and outperforms existing methods.
We present a novel optimization method, named the Combined Optimization Method (COM), for the joint optimization of two or more cost functions. Unlike the conventional joint optimization schemes, which try to find minima in a weighted sum of cost functions, the COM explores search space for common minima shared by all …
New method reduces high-dimensional data to key features.
problem Challenges of high-dimensional data analysis and interpretability.
method Randomized search to produce subspaces, ensemble of models for variable selection.
result Outperforms existing methods in prediction and variable selection.
The increasing inclusion of Deep Learning (DL) models in safety-critical systems such as autonomous vehicles have led to the development of multiple model-based DL testing techniques. One common denominator of these testing techniques is the automated generation of test cases, e.g., new inputs transformed from the orig…
GraSP-RL uses graph neural networks to improve job shop scheduling.
problem Capturing machine-unit-job sequence relationships and managing state space growth.
method Graph neural networks for feature extraction, reinforcement learning for decision-making, decentralized optimization.
result GraSP-RL outperforms existing methods in minimizing makespan for complex production environments.
Simulated annealing improves candidate optimization for multi-objective Bayesian optimization.
problem Efficient candidate optimization for multi-objective acquisition functions in Bayesian optimization.
method Simulated annealing-based approach for batch acquisition function optimization.
result Simulated annealing outperforms SLSQP in most multi-objective optimization problems, achieving higher hypervolume values and better convergence characteristics.
Examines algorithmic modeling across three cultures.
problem Tackles algorithmic modeling in different cultural contexts.
method Uses parametric regressions, interpretable algorithms, and complex algorithms.
result Extension of Leo Breiman's thesis to include cultural differences.
Playing repeated matrix games (RMG) while maximizing the cumulative returns is a basic method to evaluate multi-agent learning (MAL) algorithms. Previous work has shown that UCB, M3, S or Exp3 algorithms have good behaviours on average in RMG. Besides, hedging algorithms have been shown to be effective on predi…
Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.
problem Selecting the best algorithm selector for a specific problem instance.
method Apply algorithm selection to the selection of other algorithms (meta-algorithm selection).
result Meta-algorithm selection can be beneficial in some cases but faces challenges in solving the meta-level problem.
Proposes CLRS benchmark to evaluate algorithmic reasoning.
problem Difficulty in transferring results across publications due to targeted algorithmic data.
method Develops a comprehensive benchmark covering various algorithmic tasks.
result Demonstrates performance of algorithmic reasoning baselines on the CLRS benchmark.
Combines multiple bandit algorithms to create a nearly optimal single algorithm.
problem Designing a single bandit algorithm that performs nearly as well as the best individual algorithm in a stochastic environment.
method Develops two general corralling algorithms that achieve favorable regret guarantees.
result The regret of the corralling algorithms is no worse than the best individual algorithm's performance.
We propose accelerated randomized coordinate descent algorithms for stochastic optimization and online learning. Our algorithms have significantly less per-iteration complexity than the known accelerated gradient algorithms. The proposed algorithms for online learning have better regret performance than the known rando…
The exchange algorithm is studied for its convergence and asymptotic variance.
problem Theoretical limitations of the exchange algorithm in sampling from doubly-intractable distributions.
method Theoretical analysis of the exchange algorithm's convergence speed and asymptotic variance.
result The exchange algorithm converges at a geometric rate and satisfies a Central Limit Theorem.
Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algori…