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ROLL | Real World Optimisation with Life-Long Learning

Real World Optimisation with Life-Long Learning. Aims & Objectives. REAL WORLD OPTIMISATION WITH LIFE-LONG LEARNING. Many practical problems arising in industrial domains concerned with operating sustainably, meeting demand and minimising costs cannot be solved exactly. Although many meta-heuristic optimisation techniques have been developed, it is clear that. New blog post outlining latest results being presented at PPSN2014. Updated results on life-long learning. About 1 year ago. About 1 year ago.

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Edinburgh Napier University

Emma Hart

Edinburgh●●●●●●●●●niversity

Edi●●●rgh , City iof Edinburgh, EH1095DT

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Edinburgh Napier University

Emma Hart

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GB

44.1●●●●2783

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ROLL | Real World Optimisation with Life-Long Learning | rollproject.org Reviews

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Real World Optimisation with Life-Long Learning. Aims & Objectives. REAL WORLD OPTIMISATION WITH LIFE-LONG LEARNING. Many practical problems arising in industrial domains concerned with operating sustainably, meeting demand and minimising costs cannot be solved exactly. Although many meta-heuristic optimisation techniques have been developed, it is clear that. New blog post outlining latest results being presented at PPSN2014. Updated results on life-long learning. About 1 year ago. About 1 year ago.

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2 menu

3 skip to content

4 blog

5 benchmark problems

6 bin packing

7 job shop scheduling

8 vehicle routing

9 publications

10 collaborators

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ROLL | Real World Optimisation with Life-Long Learning | rollproject.org Reviews

https://rollproject.org

Real World Optimisation with Life-Long Learning. Aims & Objectives. REAL WORLD OPTIMISATION WITH LIFE-LONG LEARNING. Many practical problems arising in industrial domains concerned with operating sustainably, meeting demand and minimising costs cannot be solved exactly. Although many meta-heuristic optimisation techniques have been developed, it is clear that. New blog post outlining latest results being presented at PPSN2014. Updated results on life-long learning. About 1 year ago. About 1 year ago.

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rollproject.org

1

PPSN 2014 news | ROLL

http://rollproject.org/364

Real World Optimisation with Life-Long Learning. Aims & Objectives. A new paper on the life-long learning capabilities of NELLI* accepted for publication at PPSN 2014. This entry was posted in News. May 27, 2014. Workshop on Real-World Problems@GECCO 2014 →. Workshop on the Mathematical Modelling of the Risk of Wind Damage to Forests. AHDB Smart Agriculture 2015. New blog post outlining latest results being presented at PPSN2014. Updated results on life-long learning. About 2 years ago. About 2 years ago.

2

GECCO 2014 news | ROLL

http://rollproject.org/gecco-2014-news

Real World Optimisation with Life-Long Learning. Aims & Objectives. The ROLL team will be presenting three papers at GECCO 2014 in Vancouver. An Improved Immune Inspired Hyper-Heuristic for Combinatorial Optimisation Problems. A Real-World Employee Scheduling and Routing Application. Hot Off the Press Track. A Lifelong Learning Hyper-Heuristic for Bin-Packing. Evolutionary Computation, MIT Press. This entry was posted in News. April 25, 2014. Evidence of Lifelong learning during optimisation.

3

Meet the team | ROLL

http://rollproject.org/meet-the-team

Real World Optimisation with Life-Long Learning. Aims & Objectives. Kevin Sim is a researcher who works in the field of hyper-heuristics and problem-classification algorithms, and works with Prof. Hart on a large national grant on hyper-heuristics. He is also co-chair of a workshop at Evo-Star 2013 called EvoIndustry. Funded by EPSRC: EP/J021628/1. Supported by Edinburgh Napier University's. Institute for Informatics and Digital Innovation.

4

Updated results on life-long learning | ROLL

http://rollproject.org/updated-results-on-life-long-learning

Real World Optimisation with Life-Long Learning. Aims & Objectives. Updated results on life-long learning. Two recent papers, published at GECCO 2014. Described some improved results from our NELLI system, whose goal is to provide fast, reasonable solution to problems that are presented to the system in a continuous stream, possibly changing in characteristics over time. NELLI has two novel features:. A mechanism to continuously generate novel heuristics to solve problems. The new representation uses a v...

5

Collaborators | ROLL

http://rollproject.org/collaborators

Real World Optimisation with Life-Long Learning. Aims & Objectives. Optrak Distribution Software Ltd is one of the UK’s main producers of Vehicle Routing Software. Customers include 3663, MenziesDistribution and Paperlinx and are based in the UK, Germany, Spain, Portugal, Ireland, USA and Canada. Its products are used to plan transportation of products such as newspapers, lubricant oils, food and waste products. Website: http:/ optrak.com/. Prof Barry Gardiner, INRA & Forest Research. About 2 years ago.

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ROLL | Real World Optimisation with Life-Long Learning

Real World Optimisation with Life-Long Learning. Aims & Objectives. REAL WORLD OPTIMISATION WITH LIFE-LONG LEARNING. Many practical problems arising in industrial domains concerned with operating sustainably, meeting demand and minimising costs cannot be solved exactly. Although many meta-heuristic optimisation techniques have been developed, it is clear that. New blog post outlining latest results being presented at PPSN2014. Updated results on life-long learning. About 1 year ago. About 1 year ago.

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