Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

/pdf/hyper-heuristics-a-survey-of-the-state-of-the-art-mwp7dhxfb3.pdf

Hyper-heuristics: a survey of the state of the art

Waste collection vehicle routing problem with time windows

The new era of Web and Internet of Things (IoT) paradigm is being enabled by the proliferation of various devices like RFIDs, sensors, and actuators. Smart devices (devices having significant computational capabilities, transforming them to ‘smart things’) are embedded in the environment to monitor and collect ambient information. In a city, this leads to Smart City frameworks. Intelligent services could be offered on top of such information related to any aspect of humans’ activities. A typical example of services offered in the framework of Smart Cities is IoT-enabled waste management. Waste management involves not only the collection of the waste in the field but also the transport and disposal to the appropriate locations. In this paper, we present a comprehensive and thorough survey of ICT-enabled waste management models. Specifically, we focus on the adoption of smart devices as a key enabling technology in contemporary waste management. We report on the strengths and weaknesses of various models to reveal their characteristics. This survey sets up the basis for delivering new models in the domain as it reveals the needs for defining novel frameworks for waste management.

Challenges and Opportunities of Waste Management in IoT-Enabled Smart Cities: A Survey

We study a waste collection vehicle routing problem with time windows (VRPTW) complicated by multiple disposal trips and driver's lunch breaks. Recently Kim et al. [1] introduced and addressed this problem using an extension of the well-known Solomon's insertion approach, and a clustering-based algorithm. We propose and present the results of an initial study of a multi-objective genetic algorithm for the waste collection VRPTW using a set of benchmark data from real-world problems obtained by Kim et al.

/pdf/waste-collection-vehicle-routing-problem-with-time-windows-21mp6a6kkz.pdf

Waste collection vehicle routing problem with time windows using multi-objective genetic algorithms

This project utilizes the evolutionary process found in Genetic Programming to evolve an improved decision formula for the Ant System algorithm. Two such improved formulae are discovered, one which uses the typical roulette wheel selection found in all well-known Ant Colony Optimization algorithms, and one which uses a greedy-style selection mechanism. The evolution of each formula is trained using the Ant System algorithm to solve a small Travelling Salesman Problem (TSP) and tested using larger unseen TSP instances.

/pdf/evolving-an-edge-selection-formula-for-ant-colony-39rnxw7did.pdf

Evolving an edge selection formula for ant colony optimization

In this paper we examine the predictability of genetic algorithm (GA) performance using information-theoretic fitness landscape measures. The outcome of a GA is largely based on the choice of search operator, problem representation and tunable parameters (crossover and mutation rates, etc). In particular, given a problem representation the choice of search operator will determine, along with the fitness function, the structure of the landscape that the GA will search upon. Statistical and information theoretic measures have been proposed that aim to quantify properties (ruggedness, smoothness, etc) of this landscape. In this paper we concentrate on the utility of information theoretic measures to predict algorithm output for various instances of the capacitated and time-windowed vehicle routing problem. Using a clustering-based approach we identify similar landscape structures within these problems and propose to compare GA results to these clusters using performance profiles. These results highlight the potential for predicting GA performance, and providing insight self-configurable search operator design.

/pdf/predicting-genetic-algorithm-performance-on-the-vehicle-55r84naypz.pdf

Predicting genetic algorithm performance on the vehicle routing problem using information theoretic landscape measures

Control of the flow of information in large-scale non-deterministic social networks is a complex problem requiring both a search for the optimal connection of a control system to the network, and a means of determining the required control signals. This paper formalizes the Network Control Problem (NCP) as a means of relating the field of diverse social network control subproblems. Additionally, this paper defines a novel NCP subproblem, the \(\theta \)-Consensus Avoidance Problem (\(\theta \)-CAP), as a next step towards solving the general NCP. Benchmark results for the \(\theta \)-CAP using Artificial Neural Networks, Evolutionary Neural Networks, and heuristic methods are presented, and interesting areas of the problem space are identified.

Towards intelligent control of influence diffusion in social networks

We address a large scale waste collection vehicle routing problem with time windows (VRPTW) complicated by multiple disposal trips and drivers lunch breaks. A fitness search space analysis for several problem instances of the waste collection VRPTW utilizing both statistical and information theoretic measures is presented. We find that mutation landscapes were more easily distinguishable than crossover landscapes. An interesting result was that the information theoretic results seem similar for all the operators indicating the general structure of the landscapes are very similar.

/pdf/a-search-space-analysis-for-the-waste-collection-vehicle-43e9pjujs3.pdf

A search space analysis for the waste collection vehicle routing problem with time windows

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Andrew Runka

Author Tools

Chat about Author