Knowledge in Memetic Algorithms for Stock Classification

2014 ◽  
Vol 4 (1) ◽  
pp. 13-29 ◽  
Author(s):  
Jie Du ◽  
Roy Rada
Keyword(s):  
Evolutionary Computation ◽  
Domain Knowledge ◽  
Memetic Algorithm ◽  
Search Algorithm ◽  
Financial Statements ◽  
Memetic Algorithms ◽  
Semantic Knowledge ◽  
Optimal Solutions ◽  
Test Bed ◽  
Knowledge Based

This paper introduces a framework for a knowledge-based memetic algorithm, called KBMA. The problem of stock classification is the test bed for the performance of KBMA. Domain knowledge is incorporated into the initialization and reproduction phases of evolutionary computation. In particular, the structure of financial statements is used to sort the attributes, which contributed to a faster convergence on near optimal solutions. A semantic net is used to measure the distance between parents and offspring. Two case studies were implemented, in which domain knowledge is used to constrain the reproductive operators so that the offspring is semantically dissimilar (or similar) to the parent. The results show that KBMA outperformed the random memetic algorithm in the former case but did not in the latter case. The interpretation of the results is that when the search algorithm is distant from its goal, making large steps as defined by the semantic knowledge is helpful to the search.

scholarly journals Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

2016 ◽  
Vol 7 (1) ◽  
pp. 56-77 ◽  
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu
Keyword(s):  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Task Planning ◽  
Relational Models ◽  
Markov Logic ◽  
Plan Generation ◽  
Knowledge Based ◽  
Probabilistic Values ◽  
Robot Task ◽  
Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

A Mixed-Integer Memetic Algorithm Applied to Batch Process Optimization

2013 ◽  
Vol 300-301 ◽  
pp. 645-648 ◽  
Author(s):  
Yung Chien Lin
Keyword(s):  
Memetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Memetic Algorithms ◽  
Batch Processes ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Genetic Operators ◽  
Search Methods

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a mixed-integer memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and applied to the optimal design of batch processes. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

scholarly journals Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

2020 ◽  
pp. 1097-1120
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael S. Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu
Keyword(s):  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Task Planning ◽  
Relational Models ◽  
Markov Logic ◽  
Plan Generation ◽  
Knowledge Based ◽  
Probabilistic Values ◽  
Robot Task ◽  
Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

A Memetic Algorithm for Mixed-Integer Optimization Problems

2013 ◽  
Vol 284-287 ◽  
pp. 2970-2974
Author(s):  
Yung Chien Lin
Keyword(s):  
Constrained Optimization ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Memetic Algorithms ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Genetic Operators ◽  
Mixed Integer Optimization ◽  
Search Methods

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on a benchmark mixed-integer constrained optimization problem. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

scholarly journals Semantic knowledge based reasoning framework for human robot collaboration

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 373-378
Author(s):  
Sharath Chandra Akkaladevi ◽  
Matthias Plasch ◽  
Michael Hofmann ◽  
Andreas Pichler
Keyword(s):  
Semantic Knowledge ◽  
Knowledge Based ◽  
Human Robot Collaboration

scholarly journals An evolutionary approach to the vehicle route planning in e-waste mobile collection on demand

2021 ◽  
Vol 25 (8) ◽  
pp. 6665-6680
Author(s):  
Krzysztof Szwarc ◽  
Piotr Nowakowski ◽  
Urszula Boryczka
Keyword(s):  
Tabu Search ◽  
Quality Evaluation ◽  
Memetic Algorithm ◽  
Route Planning ◽  
Memetic Algorithms ◽  
Approximate Methods ◽  
On Demand ◽  
Suboptimal Solutions ◽  
Novel Method ◽  
The Given

AbstractThe article discusses the utilitarian problem of the mobile collection of waste electrical and electronic equipment. Due to its $$\mathcal {NP}$$ NP -hard nature, implies the application of approximate methods to discover suboptimal solutions in an acceptable time. The paper presents the proposal of a novel method of designing the Evolutionary and Memetic Algorithms, which determine favorable route plans. The recommended methods are determined using quality evaluation indicators for the techniques applied herein, subject to the limits characterizing the given company. The proposed Memetic Algorithm with Tabu Search provides much better results than the metaheuristics described in the available literature.

Configuration Tree Solver: A Technology for Automated Design and Configuration

1990 ◽  
Author(s):  
Alexander Kott ◽  
Gerald Agin ◽  
Dave Fawcett
Keyword(s):  
Problem Solving ◽  
General Model ◽  
General Class ◽  
Domain Knowledge ◽  
Automated Design ◽  
Automotive Component ◽  
Business Operation ◽  
Knowledge Based ◽  
Specific Configuration

Abstract Configuration is a process of generating a definitive description of a product or an order that satisfies a set of specified requirements and known constraints. Knowledge-based technology is an enabling factor in automation of configuration tasks found in the business operation. In this paper, we describe a configuration technique that is well suited for configuring “decomposable” artifacts with reasonably well defined structure and constraints. This technique may be classified as a member of a general class of decompositional approaches to configuration. The domain knowledge is structured as a general model of the artifact, an and-or hierarchy of the artifact’s elements, features, and characteristics. The model includes constraints and local specialists which are attached to the elements of the and-or-tree. Given the specific configuration requirements, the problem solving engine searches for a solution, a subtree, that satisfies the requirements and the applicable constraints. We describe an application of this approach that performs configuration and design of an automotive component.

Predicting readers’ domain knowledge based on eye-tracking measures

2018 ◽  
Vol 36 (6) ◽  
pp. 1027-1042 ◽  
Author(s):  
Quan Lu ◽  
Jiyue Zhang ◽  
Jing Chen ◽  
Ji Li
Keyword(s):  
Logistic Regression ◽  
Eye Tracking ◽  
Information Search ◽  
Domain Knowledge ◽  
Fixation Duration ◽  
Baseline Model ◽  
Content Type ◽  
Knowledge Based ◽  
Total Fixation ◽  
Negative Predictor

Purpose This paper aims to examine the effect of domain knowledge on eye-tracking measures and predict readers’ domain knowledge from these measures in a navigational table of contents (N-TOC) system. Design/methodology/approach A controlled experiment of three reading tasks was conducted in an N-TOC system for 24 postgraduates of Wuhan University. Data including fixation duration, fixation count and inter-scanning transitions were collected and calculated. Participants’ domain knowledge was measured by pre-experiment questionnaires. Logistic regression analysis was leveraged to build the prediction model and the model’s performance was evaluated based on baseline model. Findings The results showed that novices spent significantly more time in fixating on text area than experts, because of the difficulty of understanding the information of text area. Total fixation duration on text area (TFD_T) was a significantly negative predictor of domain knowledge. The prediction performance of logistic regression model using eye-tracking measures was better than baseline model, with the accuracy, precision and F(β = 1) scores to be 0.71, 0.86, 0.79. Originality/value Little research has been reported in literature on investigation of domain knowledge effect on eye-tracking measures during reading and prediction of domain knowledge based on eye-tracking measures. Most studies focus on multimedia learning. With respect to the prediction of domain knowledge, only some studies are found in the field of information search. This paper makes a good contribution to the literature on the effect of domain knowledge on eye-tracking measures during N-TOC reading and predicting domain knowledge.

Analyzing self-★ island-based memetic algorithms in heterogeneous unstable environments

2016 ◽  
Vol 32 (5) ◽  
pp. 676-692 ◽  
Author(s):  
Rafael Nogueras ◽  
Carlos Cotta
Keyword(s):  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Population Based ◽  
Self Healing ◽  
Computational Power ◽  
Ability To Work ◽  
Computing Power ◽  
Simulated Environment ◽  
Computational Resources ◽  
Better Than

Computational environments emerging from the pervasiveness of networked devices offer a plethora of opportunities and challenges. The latter arise from their dynamic, inherently volatile nature that tests the resilience of algorithms running on them. Here we consider the deployment of population-based optimization algorithms on such environments, using the island model of memetic algorithms for this purpose. These memetic algorithms are endowed with self-★ properties that give them the ability to work autonomously in order to optimize their performance and to react to the instability of computational resources. The main focus of this work is analyzing the performance of these memetic algorithms when the underlying computational substrate is not only volatile but also heterogeneous in terms of the computational power of each of its constituent nodes. To this end, we use a simulated environment that allows experimenting with different volatility rates and heterogeneity scenarios (that is, different distributions of computational power among computing nodes), and we study different strategies for distributing the search among nodes. We observe that the addition of self-scaling and self-healing properties makes the memetic algorithm very robust to both system instability and computational heterogeneity. Additionally, a strategy based on distributing single islands on each computational node is shown to perform globally better than placing many such islands on each of them (either proportionally to their computing power or subject to an intermediate compromise).

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