Chemical Physics Letters 483 (2009) 164–167

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Chemical Physics Letters

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Learning from evolutionary optimization by retracing search paths

Peter van der Walle a,*, Janne Savolainen b, L. Kuipers a,b, Jennifer L. Herek b

a FOM Institute for Atomic and Molecular Physics [AMOLF], Science Park 113, 1098 XG Amsterdam, The Netherlandsb Optical Sciences Group, Department of Science and Technology, MESA+ Institute for Nanotechnology, University of Twente, 7500 AE Enschede, The Netherlands

a r t i c l e i n f o

Article history:Received 20 May 2009In final form 14 October 2009Available online 17 October 2009

0009-2614/$ - see front matter � 2009 Elsevier B.V. Adoi:10.1016/j.cplett.2009.10.049

* Corresponding author. Fax: +31 207547290.E-mail address: p.v.d.walle@amolf.nl (P. van der W

a b s t r a c t

Evolutionary search algorithms are used routinely to find optimal solutions for multi-parameter prob-lems, such as complex pulse shapes in coherent control experiments. The algorithms are based on evolv-ing a set of trial solutions iteratively until an optimum is reached, at which point the experiment ends.We have extended this approach by recording the best solution in each iteration and subsequently apply-ing these to a modified system. By studying the shape of the learning curves in different systems, featuresof the fitness landscape are revealed that aid in deriving the underlying control mechanisms. We illus-trate our method with two examples.

� 2009 Elsevier B.V. All rights reserved.

1. Introduction

One of the central goals of coherent control is to extract new in-sights into molecular photophysical processes, by studying thepulse shapes that can effectively steer a system [1]. For the controlof complex systems, in which many pathways compete, a broadbandwidth and a high spectral resolution are required to encodeindividual phases for the different pathways. For this purpose, li-quid crystal pulse shapers have been developed, in which a maskcontaining hundreds of pixels spans the laser spectrum in the fou-rier-plane of a 4-f geometry [2]. With this many degrees of free-dom, finding an effective control pulse is a difficult task. Hence,the pulse shaper is often incorporated in a learning loop, guidedby an evolutionary algorithm, in order to find the optimal pulseshape for the control task at hand [3]. The loop optimizes a singleexperimentally determined feedback value, the fitness. Whilethese learning loops may easily converge on pulse shapes withhigh yields, the resulting optimal pulse is often exceedingly com-plex. The complexity of the optimal pulse shape tends to impedethe interpretation of the mechanism underlying the control.

Here we report a simple and general technique that supple-ments learning loop experiments and provide a handle for inter-pretation. The method is based on recording and saving the pulseshapes along a learning curve and subsequently repeating the mea-surement of the fitnesses corresponding to the application of thesepulses on the system of interest. In the repeated measurements, asingle parameter in the system is systematically varied in order tostudy its effect on the shape of the learning curve and/or optimiza-tion yield.

Fig. 1 represents schematically two systems with slightly differ-ent fitness landscapes (zooming in at a peak). An optimization

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alle).

experiment is performed on system 1, ending at a maximum forthis system. Then, using the retracing strategy, the pulse shapes re-corded along the learning curve of this optimization are reappliedand measured on system 2. The difference in the shape of theresulting learning curve shows directly that the fitness landscapescannot be compared. If random pulse shapes would be used in-stead, they are likely to fall outside of the peak for both systems,such that the comparison of the landscapes would be inconclusive.

We will illustrate how this method can be applied to gain in-sight into the system with two examples. First, using an artificiallight harvesting complex in which we showed control over energyflow pathways [4], we will use the retracing strategy with variedlaser fluence to distinguish two different control mechanisms. Inthe second example, the stimulated emission from a solvated dyewas controlled [5], and by retracing the learning curve in differentsolvents we extracted insight on the effect of the surrounding envi-ronment. The retracing strategy is used to extend the range of sol-vents to a polar solvent, in which a different mechanism isobserved.

Our method does not depend on the specific search algorithm.The only requirement is that it can successfully optimize a selectedfitness value. We used the CMA-ES with weighted recombinationfor our optimizations [6]. The algorithm was setup to use a gener-ation containing 40 pulse shapes of which the best 20 were recom-bined, weighted by their fitness value, into a new parent for thenext generation. Due to this large number of pulse shapes takenin the recombination, the optimization is extremely robust to noiseon the measured fitness value [7].

The phase function on the mask was described by the sum ofthree spline interpolants. A course spline with a spacing of 40 pix-els and a range of 3p, a spline with a spacing of 20 pixels and arange of 1.5p and a fine spline with a spacing of 10 pixels and arange of .75p. The initial step-size was set to 10% of the range ofthe parameters.

Fig. 1. Fitness landscapes (left) are compared by sampling with fixed pulse shapes. The pulse shapes are chosen along the learning curve of a successful optimization (right) toensure a difference in response between the pulse shapes.

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Both experiments use a pump–probe setup with a shaped pumppulse and an unmodulated white light probe pulse [8]. The feed-back signal for the evolutionary algorithm is calculated by integrat-ing a spectral window in a transient absorption spectrum,measured at a fixed time delay. In both examples only the phaseof the pulse is modulated, leaving the pulse energy constant.

2. Distinguishing control mechanisms

The first sample is an artificial light-harvesting complex thatclosely mimics the early-time photophysics of a natural light-har-vesting protein (LH2) [8]. The complex consists of a single donor(carotenoid) and acceptor (porphyrin) moiety [9], thus signifi-cantly reducing the structural complexity compared to LH2 [10].In the initial coherent control experiment [4] the target objectivewas to manipulate the branching ratio between the functional en-ergy transfer (ET) channel and the internal conversion (IC) losschannel after excitation of the donor at 510 nm. The signalsbelonging to ET and IC were resolved from the transient spectrummeasured at 8 ps time delay and their ratio was used as feedbackfor the optimization. The signature of the ET is a negative bandat 700 nm, while a positive band around 610 nm is used as a mea-sure of the IC. These bands correspond to the bleach of the porphy-rin moiety and excited state absorption from a lower lying state inthe carotenoid, respectively.

Fig. 2 shows the learning curve from the original optimization(circles) having a total increase of the IC/ET ratio of some 20%: aninitial jump of�10% plus a gradual learning part providing an addi-tional 10% improvement. Using the retracing strategy, we reap-plied the pulse shapes of this learning curve, using ’identical’starting pulses (i.e. the same energy and spectral bandwidth) pre-pared two days later (open squares). With the exception of a fewoutliers caused by occasional laser instabilities, the agreement be-tween the curves is good, which verifies the reproducibility of the

experiment. Note that the learning curve presented in Fig. 2 re-sulted from one of many closed-loop optimizations aimed atincreasing the ratio IC/ET. Despite subtle variations, the learningcurves were all found to share two characteristic features: an ini-tial ‘jump’, followed by a further, gradual enhancement.

To allow for the possibility of multi-photon control schemes,the experiments on the light-harvesting complexes were per-formed using laser fluences that partially saturate the carotenoidS0 ? S2 transition. As a result, a trivial control mechanism in whichpartial saturation is avoided simply by stretching the pulse in timeproduces the observed jump in the learning curves, but is not thecause of the subsequent gradual growth of the IC/ET ratio, as veri-fied by repeating the measurement of the fitness curve with vary-ing laser fluences. Fig. 2 also shows the learning curve retracedwith only half of the original fluence (now 250 nJ, triangles). Fittingthe data with a monotonically increasing function, we find that thesame curve fits both measurements when offset to the first gener-ation after the jump (Fig. 2, grey lines). This result shows that thejump and the ‘learning’ part have markedly different fluencedependencies: the initial jump decreases to approximately half ofits amplitude when the fluence is halved, whereas the increase inthe IC/ET ratio remains the same for the learning part of the curve(�10%).

To better understand the scaling with excitation fluence, wesimulated the saturation of the S0 ? S2 transition to find out therole it plays in the measured IC and ET signals, and in the controlresults. In general, using a pulse shape that is stretched in time willgive the photons in the trailing end of the pulse a probability to re-excite molecules that have had time to relax back to the groundstate after transferring their energy to the acceptor. In this case,the excited state lifetime is on the order of 100 fs [8], hence astretched pulse at high laser fluence can significantly increasethe number of excitation processes, leading to an increase in themeasured signal compared to the short pulse. Since the pulse anal-ysis of the optimizations revealed a pulse-train structure [4], we

Fig. 2. Learning curve for the original optimization (circles) and two retracedcurves, one at the original laser fluence (squares) and one at half the fluence(triangles).

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simulated the magnitude of the IC and ET signals using a pulsetrain with 7 sub-pulses together with the experimental saturationcurves of IC and ET (Fig. 3, inset). The simulation was made usingan incoherent model of the dyad derived by fitting the transientabsorption surface measured via pump–probe [8]. Note that, inthe limit of a transform-limited excitation pulse, the same transi-tion (carotenoid S0 ? S2) is responsible for the saturation of bothsignals and an increase in the signal of IC brings a concommitantincrease in the ET signal. However for the simulated shaped pulses,Fig. 3 shows that the ratio IC/ET increases as a function of the sub-pulse spacing. The origin of the variation of the IC/ET ratio withpulse separation is the fast energy transfer within the dyad. Thedonor passes its energy quickly to the acceptor, after which the do-nor can be re-excited. The re-excitation only increases the signal inthe IC band, as the level representing the ET band is long-lived.

The original optimization was made with a pulse energy of500 nJ per pulse. At this and lower energies, the IC/ET ratio in-creases nearly linearly with pulse separation. With the usedparametrization for the phase function on the pulse shaper, a ran-dom pulse will have a multi-pulse structure with an envelope cor-

Fig. 3. Simulated ratio IC/ET as a function of pulse separation with increasing pulseenergies: 50, 100, 250, 500, 1000, and 2000 nJ/pulse (asterisk, square, delta, triangle,circle, and diamond, respectively). Inset: Experimental saturation curves with TLpulse for the IC (circle) and ET (square) signals and exponential fit (grey lines).

responding to a train of 7 pulses with sub-pulse spacing of about900 fs. The curve corresponding to 500 nJ/pulse shows a 10% in-crease in the IC/ET ratio at this spacing, consistent with the initialjump observed in the optimization.

Due to the random starting position of the search algorithm, thefirst pulse shapes will invariably be much longer than the trans-form-limited pulse, thereby creating an increase in the fitness viaa trivial and incoherent control mechanism that merely avoids sat-uration. The subsequent learning part has a different scaling withexcitation energy than the initial jump. In fact, the relative increaseof the fitness in this part of the curve does not depend on the laserpower. This scaling cannot be explained using the incoherent mod-el of the dyad and it originates from an active coherent controlmechanism over the branching of the energy flow (see [8]).

3. Comparing fitness landscapes and discriminatingmechanisms

We now turn to a second example, in which we demonstratehow repeated measurements can be used to compare fitness land-scapes. We studied the control of the stimulated emission fromCoumarin 6. The original optimization was made in cyclohexane.As feedback, signal from an integrated spectral window (510–595 nm) from a transient absorption spectrum measured at 1 nsdelay was used, corresponding to the stimulated emission bandof the dye. The excitation pulse energy was 300 nJ/pulse and thespectrum centered around 475 nm, in the red tail of the absorptionspectrum. The learning curve of the optimization performed incyclohexane was remeasured in a range of solvents (the non-polarhexane, octane, decane and polar acetone), and the effect of thesolvent on the fitness landscape was derived from the shapes ofthe obtained curves.

The fitness curves for the repeated optimizations in the differ-ent solvents were normalized to the fitness of the transform-lim-ited pulse, so that the yield of the control could be compareddirectly (Fig. 4). To test if the fitness landscapes are topologicallythe same, we fit the curves simultaneously. One curve was de-scribed directly by the number of fitness values, while the othercurves were described by the same values multiplied by a scalingfactor. Taking into account the noise (�2% for decane and octane,�5% for hexane due to a lower solubility) the v2 is 1.3. In orderto test our fit procedure, we simulated data sets based on a model

Fig. 4. Normalized fitness curves for the optimization of stimulated emission ofCoumarin 6 in cyclohexane (grey dots), remeasured in different solvents, hexane(triangles), octane (squares), and decane (circles). The dashed lines are the result ofa global fit that describes the curves with the same values, only scaled in amplitude.

Fig. 5. Normalized fitness curves of the optimization in cyclohexane, remeasured inacetone at varying excitation pulse energies. The stimulated emission is reducedinstead of enhanced in this solvent, especially at increasing pulse energy.

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in which the shapes of different landscapes vary only by an ampli-tude scaling factor. The test data resulted in a distribution of v2

values with 95% of the values lower than 1.7. As the v2 for the fitto the experimental data is well below 1.7, this model clearly de-scribes the data very well. The similarity in topology of the fitnesslandscapes shows that the photophysics are only weakly perturbedby the non-polar solvation, and that the features of the fitnesslandscapes are the same for all solvents.

In contrast, when the fitness curve obtained from the optimiza-tion in cyclohexane is remeasured with the dye dissolved in ace-tone—a polar solvent—the shape of the curve changesdramatically (Fig. 5). At low pulse energy there is no effect onthe stimulated emission for any pulse shape along the curve. Atincreasing pulse energy, the direction of the fitness curve is re-versed; instead of increasing, the stimulated emission is decreasedby the shaped pulses. This effect is due to the shape of the optimalpulse—a down-chirp [5]—that limits the excitation via a 2-photonpump-dump process [11]. The observation of this non-linear effectis due to the significant red shift of the absorption spectrum of thedye in acetone, such that the peak of the absorption cross-sectionnow overlaps with the laser spectrum. The excitation is well out-side the linear regime for the higher laser fluences, facilitatingthe pump-dump mechanism.

The retracing technique is a powerful method to detect differ-ences in the control mechanism upon variation of the experimentalconditions. Independent optimizations can produce dramaticallydifferent learning curves, even when run with identical conditions.Furthermore, if after different optimizations, different optimalpulse shapes are found, it is possible that the algorithm was simplyattracted to a different (local) maximum, while the global fitnesslandscapes are the same. Using the retracing technique, the land-scapes of different systems can be consistently sampled and com-pared, allowing discrimination of control mechanisms byinspection of remeasured learning curves.

In the case of the solvated dye, the remeasured learning curvesindicate that the fitness landscape has the same shape in all testednon-polar solvents and that the result of the optimal pulse shapecan be compared directly. Although in some cases comparing thefitness of the transform-limited pulse to that obtained with thebest pulse may suffice in bringing further understanding of thecontrol [12], we have shown that the shape of the learning curvecan provide additional information: the saved pulse shapes mapout a path through the multidimensional search space and the fit-ness curve depicts the value of a chosen physical parameter alongthis path. Generally, variations in the shape of the fitness curve inthe repeated experiments indicate that the fitness landscape is dif-ferent, and therefore the underlying physical control mechanismhas changed.

We have presented a simple technique that complements evo-lutionary control experiments and that can ultimately be em-ployed to utilize the obtained optimization results to extractfurther information on the molecular mechanisms and how theyare influenced by external parameters. The power of repeated mea-surements has been demonstrated with two examples. A trivialand a non-trivial learning part in the control of the artificiallight-harvesting complex have been separated. The difference be-tween the photophysics in polar and non-polar solvents was ob-served from the learning curve and, for the non-polar solvents, aclear trend has been identified in the optimization of stimulatedemission from a dye. These examples show how the technique en-ables a direct comparison of the outcome of an optimization whena single parameter is systematically varied. In addition, the re-peated measurements verify the reproducibility of the optimiza-tion results. In this paper, we have applied the technique to acoherent control experiment. However, it can also be applied inany other optimization experiment based on a stochastic searchalgorithm.

Acknowledgements

This work is part of the research program of the ‘Stichting voorFundamenteel Onderzoek der Materie (FOM)’, which is financiallysupported by the ‘Nederlandse organisatie voor WetenschappelijkOnderzoek (NWO)’.

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