A Futurist approach to dynamic environments Jano van Hemert, Leiden - - PDF document

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A “Futurist” approach to dynamic environments

Jano van Hemert, Leiden University, The Netherlands & Clarissa Van Hoyweghen, University of Antwerp, Belgium & Eduard Lukschandl, Ericsson & Hewlett-Packard & Katja Verbeeck, University of Brussels, Belgium

presented by Jano van Hemert jvhemert@liacs.nl http://www.liacs.nl/~jvhemert

Introduction 2 ✬ ✫ ✩ ✪

How it all started

Coil Summer School 2000, Limerick, Ireland ☞ People assigned to groups to solve different problems ☞ Conor Ryan provided our group with two dynamic problems ☞ He has attempted to solve those problems using diploid chromosomes ☞ Our objective set was to try to solve them using one of the techniques presented at the summer school

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Introduction 3 ✬ ✫ ✩ ✪

How it all started

Coil Summer School 2000, Limerick, Ireland

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Introduction 4 ✬ ✫ ✩ ✪

The next half hour

① Problem descriptions ② General idea ③ Two tested implementation ④ Experiments & Results ⑤ Conclusions & Future Work ⑥ Questions & Discussion

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Problem descriptions 5 ✬ ✫ ✩ ✪

0 − 1 Knapsack – Definition

✔ Goal is to fill a knapsack with objects ✔ Each object has a weight and value assigned ✔ Every 15 generations the maximum allowed weight is changed ✔ Maximum weight is switched between 50% and 80% of the total weight

• f all the objects

✔ Total of 400 generations (time steps) is used

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Problem descriptions 6 ✬ ✫ ✩ ✪

0 − 1 Knapsack – Behaviour

70 80 90 50 100 150 200 250 300 350 400

• ptimal value
• generations

☞ Optimum changes over time

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Problem descriptions 7 ✬ ✫ ✩ ✪

O˘ smera’s function — Definition

g1(x, t) = 1 − e200(x−c(t))2 with c(t) = 0.04(⌊t/20⌋), x ∈ {0.000, . . . 2.000}, each time step t ∈ {0, . . . 1000} equal to one generation

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Problem descriptions 8 ✬ ✫ ✩ ✪

O˘ smera’s function — Behaviour

0.5 1 1.5 2 x 200 400 600 800 t 0.5 1 g(x,t) 0.05 0.1 0.15 0.2 0.25 20 40 60 80 100 c(t)

• t

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General idea 9 ✬ ✫ ✩ ✪

Predicting the future

f(x,t) t t+∆

maxgen

acquire fitness values regress fitness predictor use predictor for future population

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General idea 10 ✬ ✫ ✩ ✪

Learning from the future migration

current population future population

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General idea 11 ✬ ✫ ✩ ✪

Parameters

✔ m determines how many individuals are copied to the current population, best m from the future are selected and overwrite the worst m in the current population ✔ ∆ determines how many generations ahead the future population lives

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Experimental setup 12 ✬ ✫ ✩ ✪

Two experiments

Perfect prediction ☞ Idea is that the best what could happen is that you have a perfect prediction of the future ☞ With these problems this is very easy to implement as we know exactly the optimum for t + ∆ ☞ If this is not successful, we could ask ourselves if it is useful to continue with the idea of predicting the future Noisy prediction ☞ Could the use of a predictor be harmful? ☞ We give the algorithm noisy and deceptive predictions of the future ☞ Knapsack problem gets wrong optimum (deceptive) and O˘ smera gets a random value

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Experimental & Results 13 ✬ ✫ ✩ ✪

Experimental setup

For both problems we do ✔ a test without any future population ✔ tests with four parameter settings (two pairs) for perfect prediction ✔ tests with four parameter settings (two pairs) for noisy / deceptive predictor ✔ 50 runs for each test with unique random seeds

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Experiments & Results 14 ✬ ✫ ✩ ✪

Knapsack results

predictor ∆ m error stdev best run none × × 16.6% 3.52 8.96% perfect 5 10 11.9% 3.77 4.85% perfect 15 10 20.3% 4.26 11.7% perfect 5 50 11.8% 3.70 5.97% perfect 15 50 21.4% 6.06 12.0% deceptive 5 10 12.6% 4.12 6.77% deceptive 15 10 13.0% 3.74 7.50% deceptive 5 50 12.7% 4.02 6.47% deceptive 15 50 12.8% 4.07 4.99%

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Experiments & Results 15 ✬ ✫ ✩ ✪

Knapsack results

5 10 15 20 25 50 100 150 200 250 300 350 400 Error (%)

• Generations

"knapsack_clean" 5 10 15 20 25 50 100 150 200 250 300 350 400 Error (%)

• Generations

"knapsack_perfect_d5_m10" 5 10 15 20 25 50 100 150 200 250 300 350 400 Error (%)

• Generations

"knapsack_noisy_d15_m50"

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Experiments & Results 16 ✬ ✫ ✩ ✪

O˘ smera results

predictor ∆ m error stdev best run none × × 63.8% 10.3 41.2% perfect 5 10 0.261% 0.153 0.0751% perfect 5 50 0.168% 0.148 0.0266% perfect 10 10 0.241% 0.220 0.0680% perfect 10 50 0.203% 0.099 0.0698% noisy 5 10 0.241% 0.186 0.0488% noisy 5 50 0.144% 0.122 0.0358% noisy 10 10 0.241% 0.186 0.0488% noisy 10 50 0.168% 0.148 0.0266%

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Experiments & Results 17 ✬ ✫ ✩ ✪

O˘ smera results

10 20 30 40 50 60 70 80 90 100 100 200 300 400 500 600 700 800 900 1000 Error (%)

• Generations

"osmera_clean" 0.1 0.2 0.3 0.4 0.5 0.6 200 400 600 800 1000 Error (%)

• Generations

"osmera_perfect_d5_m50" 0.1 0.2 0.3 0.4 0.5 0.6 200 400 600 800 1000 Error (%)

• Generations

"osmera_noisy_d10_m50"

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Conclusions & Future Research 18 ✬ ✫ ✩ ✪

Conclusions

Pros and cons ✘ Knapsack problem is better solved with a look-a-head time of 5 generations as opposed to 15, which is the length of the cycle ✔ Adding future predictions when solving the knapsack problem slightly improves the performance when using a deceptive function or when using small values for m ✔ Adding future predictions when solving O˘ smera’s function seems to help ✘ There is little difference in performance between using a perfect or noisy predictor... ✔ There is little difference in performance between using a perfect or noisy predictor...

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Conclusions & Future Research 19 ✬ ✫ ✩ ✪

Future Research

☞ How sensitive are the parameters m and ∆? ☞ Why does this work well for a real-valued problem and not for a problem from a discrete domain? ☞ Could we replace this whole complicated process by adding more disturbance? For instance with a high mutation rate?

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