GECCO09 - Multiobjective approach to modeling pheromone dispensers

Multiobjective Genetic Programming Approach for a Smooth Modeling of the Release Kinetics of a Pheromone DispenserEva Alfaro-Cid, Anna Esparcia-Alcázar, Pilar Moya, J.J. Merelo, Beatriu Femenia-Ferrer, Ken Sharman, Jaime Primo

Contents

Objetive
Introduction: mating disruption
Problem description
Genetic programming
Modeling results
Conclusions and future work

Objetives
Modeling the pheromone release kinetics of an experimental dispenser developed in the Centro de Ecología Química Agrícola (CEQA) of the Universidad Politécnica de Valencia.
To validate the use of multiobjective Genetic Programming for avoiding discontinuities in those time ranges where no measurements are available.

Mating disruption technique
Mating disruption by sexual confusion is an agricultural technique that intends to substitute the use of insecticides for pest control.
Sexual confusion is achieved by the diffusion of large amounts of sexual pheromone, so that the males are confused and mating is disrupted.
How? -> using pheromone dispensers
)

Pheromone dispensers
The Centro de Ecología Química Agrícola (CEQA) of the Universidad Politécnica de Valencia has developed biodegradable dispensers which work effectively during the whole flight period of the pest.

A few figures
1 kg of pheromone costs 1000 €
1 dispenser takes 200 mg of pheromone

i.e. 1 dispenser costs 20 cents (+ manufacturing)

In 1 Ha there must be 500 or 1000 dispensers (depending on the pest)
i.e cost is 100 or 200 € per Ha (+ handwork)
On the other hand,
Spraying with a classical pesticide costs 20-30 €/Ha

Problem description
Let the residual r be the percentage of product that has not been released into the atmosphere
For a given dispenser, find a function r (t), where t = time
The available data are a sequence of points (r, t) obtained in field conditions.
Since measuring r is costly, there are very few measurements available and they are not equally spaced in time (there are more measurements initially, when the speed of emission is faster).

Available data

2005 data

○ 2006 data

Genetic programming

Multi-objective genetic programming
1: A(0) = ;
2: P(0) = init_random();
3: g = 1;
4: eval (P (g-1));
5: eval (A (g-1));
6: A(g) = save( P(g-1), A(g-1));
7: truncate (A(g));
8: if g>g_max then stop;
9: M(g) = select(A(g));
10: P(g) = cross&mut(M(g));
11: g = g+1;
12: go to Step 4;

Mono vs. Multi-objective GP
Mono-objective GP
Cost function: Mean Squared Error (MSE)
MSE = 1/n * Sn (rcalculated - rmeasured)2
Multi-objective GP
Cost function (2 obj.):
Obj1 = MSE
Obj2(“Smoothness”) = M (rcalculated (t+1) – rcalculated (t))/t
Cost function (“leave-one-out”):
Obji = |rcalculated (i) – rmeasured (i)|

Comparison of results

Mono-objective best result

2-objectives best result

“Leave-one-out”bestresult

Conclusions
Genetic programming has proven to be capable of finding functions that fit well the performance of the dispensers.
The mono-objective approach obtained the smallest MSE for the training data. However, both multi-objective approaches evolved models that predicted the validation data more accurately. Probably it is a case of overfitting.
Regarding the “smoothness” of the resulting models, it can be seen that both multi-objective approaches obtained models that did not show discontinuities in their behaviour, specially the bi-objective model. This results showthe utility of including an extra objective to minimize the “jumps” in the models when dealing with problems where the experimental data are scarce.

Future work
Modeling the distributionof pheromone in the environment.
This has great economic interest, as:
it would allow the optimisation of the placement of dispensers in the plot, hence minimising the number of dispensers needed to guarantee an efficient pest control.
It would help support the claim that the amount of pheromone in the air is harmless to humans, which is needed to get governmental authorisation

Thanks!
aesparcia@iti.upv.es